Reviewerly/exHarmony huggingface dataset:
Duo Remover Cleanser / Ungku Arina Qahirah Ungku Hisham

If there is no problem, there will be no solution, and there is no reason for a new product to be existed. There is always a problem in order to build new product. With that, we introduce to the market our Duo remover cleanser that works for removing dirt on our face. Not only remove dirt, but it also can act as a cleanser. Why it is duo? It is because, the product can be worked as a makeup remover and also as a cleanser at the same time as we provide two parts in one tube. One tube is to put makeup remover liquid and the other tube is to put face cleanser. This also can be known as 2 in 1 product. Apart from that, with the existence of this product as well as the benefits, we are guaranteed that it will ease people who buys this product as it is an easy going as well as friendly user product. Our target marker is specifically made for make-up artist, traveller, men and also for Islam women out there as it comes with a small form where it will make them easier to carry everywhere they go. For example, as we can see the traveler will have problem when it comes to packing their luggage as they need to bring all of stuff along and the space will become narrow to put other important things and sometimes, they forgot to bring one of them. Moreover, Islam women will face a problem like whenever they are going out, some of them will perform prayer at the mosque where they need to bring both makeup remover and cotton pad as well as face cleanser. In addition, for make-up artist, they are likely to have more customer to do the make up on and sometimes they need to remove their customers’ make up at the same time for another reception. With that, they sometimes forgot to bring the makeup remover or cleanser along with them during work. Therefore, with these problems we created this new product by adding more value to our target market in order to overcome the problems they faced. With just one duo remover cleanser, they do not have to bring both of makeup remover and the cleanser physically as well as the cotton pad as this product provided them with two tube space and brush for it.
On Single and Multiple Representations in Dense Passage Retrieval

The advent of contextualised language models has brought gains in search effectiveness, not just when applied for re-ranking the output of classical weighting models such as BM25, but also when used directly for passage indexing and retrieval, a technique which is called dense retrieval. In the existing literature in neural ranking, two dense retrieval families have become apparent: single representation, where entire passages are represented by a single embedding (usually BERT's [CLS] token, as exemplified by the recent ANCE approach), or multiple representations, where each token in a passage is represented by its own embedding (as exemplified by the recent ColBERT approach). These two families have not been directly compared. However, because of the likely importance of dense retrieval moving forward, a clear understanding of their advantages and disadvantages is paramount. To this end, this paper contributes a direct study on their comparative effectiveness, noting situations where each method under/over performs w.r.t. each other, and w.r.t. a BM25 baseline. We observe that, while ANCE is more efficient than ColBERT in terms of response time and memory usage, multiple representations are statistically more effective than the single representations for MAP and MRR@10. We also show that multiple representations obtain better improvements than single representations for queries that are the hardest for BM25, as well as for definitional queries, and those with complex information needs.
An Empirical Analysis on Transparent Algorithmic Exploration in Recommender Systems.

All learning algorithms for recommendations face inevitable and critical trade-off between exploiting partial knowledge of a user's preferences for short-term satisfaction and exploring additional user preferences for long-term coverage. Although exploration is indispensable for long success of a recommender system, the exploration has been considered as the risk to decrease user satisfaction. The reason for the risk is that items chosen for exploration frequently mismatch with the user's interests. To mitigate this risk, recommender systems have mixed items chosen for exploration into a recommendation list, disguising the items as recommendations to elicit feedback on the items to discover the user's additional tastes. This mix-in approach has been widely used in many recommenders, but there is rare research, evaluating the effectiveness of the mix-in approach or proposing a new approach for eliciting user feedback without deceiving users. In this work, we aim to propose a new approach for feedback elicitation without any deception and compare our approach to the conventional mix-in approach for evaluation. To this end, we designed a recommender interface that reveals which items are for exploration and conducted a within-subject study with 94 MTurk workers. Our results indicated that users left significantly more feedback on items chosen for exploration with our interface. Besides, users evaluated that our new interface is better than the conventional mix-in interface in terms of novelty, diversity, transparency, trust, and satisfaction. Finally, path analysis show that, in only our new interface, exploration caused to increase user-centric evaluation metrics. Our work paves the way for how to design an interface, which utilizes learning algorithm based on users' feedback signals, giving better user experience and gathering more feedback data.
Why Adversarial Reprogramming Works, When It Fails, and How to Tell the  Difference

Adversarial reprogramming allows repurposing a machine-learning model to perform a different task. For example, a model trained to recognize animals can be reprogrammed to recognize digits by embedding an adversarial program in the digit images provided as input. Recent work has shown that adversarial reprogramming may not only be used to abuse machine-learning models provided as a service, but also beneficially, to improve transfer learning when training data is scarce. However, the factors affecting its success are still largely unexplained. In this work, we develop a first-order linear model of adversarial reprogramming to show that its success inherently depends on the size of the average input gradient, which grows when input gradients are more aligned, and when inputs have higher dimensionality. The results of our experimental analysis, involving fourteen distinct reprogramming tasks, show that the above factors are correlated with the success and the failure of adversarial reprogramming.
Kesultanan Serdang dan Jejak Peninggalannya

This article aims to discuss the history of the Serdang Sultanate Palace. The Serdang Sultanate is one of the Malay Sultanates of East Sumatra, a fraction of the Deli Sultanate, which is centered in the Province of North Sumatra. The establishment of the Serdang Sultanate Palace began when Tuanku Umar Johan Pahlawan Alam Shah Vocational Junjungan built the Tanjung Puteri Palace. Tuanku Umar was the first Sultan of Serdang to build the Tanjung Puteri Palace in 1750. The Serdang Sultanate Palace is a relic of the Sultanate that was previously built to be a resting place or throne for the Serdang Sultanate at that time, in addition to being a resting place, the Serdang Sultanate Palace was also a place to perform various activities. Activities and government systems in carrying out any activities that are closely related to the Sultanate. This research uses the historical method with an archaeological approach. The historical method is used to describe events that occurred in the past. The archaeological approach is used to find information about the relics of the Serdang Sultanate Palace as historical relics. According to Grahme Clark, who wrote his theory in Archeology and Society, archeology is a systematic study of ancient objects as a tool to reconstruct the past.
Gradient Frequency Modulation for Visually Explaining Video  Understanding Models

In many applications, it is essential to understand why a machine learning model makes the decisions it does, but this is inhibited by the black-box nature of state-of-the-art neural networks. Because of this, increasing attention has been paid to explainability in deep learning, including in the area of video understanding. Due to the temporal dimension of video data, the main challenge of explaining a video action recognition model is to produce spatiotemporally consistent visual explanations, which has been ignored in the existing literature. In this paper, we propose Frequency-based Extremal Perturbation (F-EP) to explain a video understanding model's decisions. Because the explanations given by perturbation methods are noisy and non-smooth both spatially and temporally, we propose to modulate the frequencies of gradient maps from the neural network model with a Discrete Cosine Transform (DCT). We show in a range of experiments that F-EP provides more spatiotemporally consistent explanations that more faithfully represent the model's decisions compared to the existing state-of-the-art methods.
Digitally Mediated Tools for Facilitating High-Quality Team-Based Programming Projects

In today’s digital society, many digitally mediated tools are designed, developed, and used to teach students how to program. One example is the educational programming tool Scratch, developed at MIT. However, most educational tools are designed for K-12 and CS0 to CS2 students. Relatively few are developed for advanced undergraduate or graduate students. This chapter introduces several digitally mediated tools that can be used by more advanced students in industry-like programming projects. The first tool is test skeletons, which are test methods without the test bodies. The second one is an IDE (Integrated Development Environment) plugin to record student programming steps. Then, we introduce three Internet bots, which are either open source or free to use. These bots can not only detect system-specific guideline violations but also aggregate that information (including code smells (https://en.wikipedia.org/wiki/Code_smell) and the results of test execution) on a single GitHub (https://en.wikipedia.org/wiki/GitHub) pull-request (https://en.wikipedia.org/wiki/Distributed_version_control#Pull_requests) page. These tools aim to facilitate programming and code reviewing and help student teams to write high-quality code.
Mini-Batch Consistent Slot Set Encoder for Scalable Set Encoding

Most existing set encoding algorithms operate under the implicit assumption that all the set elements are accessible, and that there are ample computational and memory resources to load the set into memory during training and inference. However, both assumptions fail when the set is excessively large such that it is impossible to load all set elements into memory, or when data arrives in a stream. To tackle such practical challenges in large-scale set encoding, the general set-function constraints of permutation invariance and equivariance are not sufficient. We introduce a new property termed Mini-Batch Consistency (MBC) that is required for large scale mini-batch set encoding. Additionally, we present a scalable and efficient attention-based set encoding mechanism that is amenable to mini-batch processing of sets, and capable of updating set representations as data arrives. The proposed method adheres to the required symmetries of invariance and equivariance as well as maintaining MBC for any partition of the input set. We perform extensive experiments and show that our method is computationally efficient and results in rich set encoding representations for set-structured data.
Training Dynamic based data filtering may not work for NLP datasets

The recent increase in dataset size has brought about significant advances in natural language understanding. These large datasets are usually collected through automation (search engines or web crawlers) or crowdsourcing which inherently introduces incorrectly labeled data. Training on these datasets leads to memorization and poor generalization. Thus, it is pertinent to develop techniques that help in the identification and isolation of mislabelled data. In this paper, we study the applicability of the Area Under the Margin (AUM) metric to identify and remove/rectify mislabelled examples in NLP datasets. We find that mislabelled samples can be filtered using the AUM metric in NLP datasets but it also removes a significant number of correctly labeled points and leads to the loss of a large amount of relevant language information. We show that models rely on the distributional information instead of relying on syntactic and semantic representations.
석개(石介)의 서문(序文) 연구(硏究)

Xuwen(序文) is consisted of various sanctions and composition. It clarifies the thesis and appeals to emotions through expression techniques such as contrast and gradation and so on. In the Xuwen(序文), Shijie(石介) emphasizes trust in historical facts and values his own perspective on literature, Confucian ideas, literary theories, and values. It secures persuasive power by simply and coherently speaking in accordance with the prose style of Zengxu(贈序) and Shuxu(書序), while showing off its writing ability through a mixture of opinions and writings, or rich emotions, or through cool-headed rationality. In addition, the accent and admiration are at their peak, while the sentences are consistent from beginning to end with one thesis through proper admiration and reflection, maximizing the literary effect. His Xuwen(序文) gains credibility based on historical truth, chooses to persuade him by appealing to emotions, or maintains objectivity in a consistent tone. In addition, he explicitly and clearly expresses his view of history, Jingxueguan(經學觀), literary theory, and political ideas through the introduction of a simple sentence. All of these are his thoroughly prepared valuable values and positive contributions to Guwen(古文) movement. Therefore, a re-evaluation of Shijie(石介)’s Xuwen(序文) should be carried out, and other prose styles should be continuously studied. This continuous study is expected to help understand the transitional phenomenon of early Song dynasty Guwen(古文) movement and to study the basis of the sentences of the breeder, including Ouyangxiu(歐陽修). In fact, Xuwen(序文) can be evaluated very positively in terms of composition, relief, plain language, and effective investigative techniques. Thus, there is room for reconsideration of a series of negative assessments of his prose being bizarre, obfuscated and unusual. In addition, it was considered to be a great sentence that is consistent with clarity of the thesis, effective composition and technique, compared to Ouyangxiu(歐陽修)’s Xuwen(序文), which has taken a step further in various sanctions, themes, and expression techniques. Xuwen(序文) by Shijie(石介) should be re-examined in the history of Chinese traiditional prose, and his influence on the Guwen(古文) movement should never be overlooked.
Data Transformer for Anomalous Trajectory Detection

Anomaly detection is an important task in many traffic applications. Methods based on deep learning networks reach high accuracy; however, they typically rely on supervised training with large annotated data. Considering that anomalous data are not easy to obtain, we present data transformation methods which convert the data obtained from one intersection to other intersections to mitigate the effort of collecting training data. The proposed methods are demonstrated on the task of anomalous trajectory detection. A General model and a Universal model are proposed. The former focuses on saving data collection effort; the latter further reduces the network training effort. We evaluated the methods on the dataset with trajectories from four intersections in GTA V virtual world. The experimental results show that with significant reduction in data collecting and network training efforts, the proposed anomalous trajectory detection still achieves state-of-the-art accuracy.
Pengembangan Sistem Penyediaan Air Bersih dengan Metode Appropriate Technology di Kelurahan Mekar Sari Kecamatan Rambutan Pemko Tebing Tinggi

Planning for a clean water supply system is a community service program carried out by USU in collaboration with the Dinas Pemberdayaan Masyarakat dan Desa(PMD) of the Pemko Tebing Tinggi. The problem encountered is the unavailability of clean water. This condition can interfere with people's daily activities such as the need for households. The clean water supply system that is implemented is the application of appropriate technology in the sense that it is easy to operate and all components are easy to care for by the public. The purpose of this activity is to provide clean water that meets health requirements, namely colorless, tasteless and odorless. This system is also planned to provide clean water for the next few years. The projection of population growth is also a consideration, which is calculated based on the average population growth data. Based on the Indonesian National Standard (SNI), the need for clean water for a population with a population of 300 households with the household category is 120 liters per person per day, so the water requirement is 1.67 liters/second. The source of water is obtained by digging 15 meters deep by installing 15 concrete rings with a diameter of 80 cm and a height of 100 cm. Then a pump is installed to suck water and then it is pumped into a poly tank (capacity 2100 liters) storage tank which is located at a height of 5-6 meters. Before flowing from the tank to the pipe, the water is filtered using a filter. For this purpose, a piping installation complete with a valve and a float is designed to automatically close the pipe when the tank is full. The result of water from this system is clean water and suitable for consumption by residents.
Hegesztőrobotok telepítésének biztonsági kérdései

The aim of this research is to summarize the safety measures related to the application of welding robots, as well as to apply them in the ÓE-Cloos Robot Welding Laboratory of Bánki Faculty at Óbuda University.This article describes the safety requirements for welding robot systems, the requirements for commissioning and safe operation.The concluding part of the dissertation presents the preparation and implementation of the installation of a new robot cell in the laboratory.
Use of Local Region Maps on Convolutional LSTM for Single-Image HDR Reconstruction

Low dynamic range (LDR) images captured by consumer cameras have a limited luminance range. As the conventional method for generating high dynamic range (HDR) images involves merging multiple-exposure LDR images of the same scene (assuming a stationary scene), we introduce a learning-based model for single-image HDR reconstruction. An input LDR image is sequentially segmented into the local region maps based on the cumulative histogram of the input brightness distribution. Using the local region maps, SParam-Net estimates the parameters of an inverse tone mapping function to generate a pseudo-HDR image. We process the segmented region maps as the input sequences on long short-term memory. Finally, a fast super-resolution convolutional neural network is used for HDR image reconstruction. The proposed method was trained and tested on datasets including HDR-Real, LDR-HDR-pair, and HDR-Eye. The experimental results revealed that HDR images can be generated more reliably than using contemporary end-to-end approaches.
The 2022 RFIC Symposium

Presents information on the 2022 RFIC Symposium.
Assessing performance of artificial neural networks and re-sampling techniques for healthcare datasets

Re-sampling methods to solve class imbalance problems have shown to improve classification accuracy by mitigating the bias introduced by differences in class size. However, it is possible that a model which uses a specific re-sampling technique prior to Artificial neural networks (ANN) training may not be suitable for aid in classifying varied datasets from the healthcare industry. Five healthcare-related datasets were used across three re-sampling conditions: under-sampling, over-sampling and combi-sampling. Within each condition, different algorithmic approaches were applied to the dataset and the results were statistically analysed for a significant difference in ANN performance. The combi-sampling condition showed that four out of the five datasets did not show significant consistency for the optimal re-sampling technique between the f1-score and Area Under the Receiver Operating Characteristic Curve performance evaluation methods. Contrarily, the over-sampling and under-sampling condition showed all five datasets put forward the same optimal algorithmic approach across performance evaluation methods. Furthermore, the optimal combi-sampling technique (under-, over-sampling and convergence point), were found to be consistent across evaluation measures in only two of the five datasets. This study exemplifies how discrete ANN performances on datasets from the same industry can occur in two ways: how the same re-sampling technique can generate varying ANN performance on different datasets, and how different re-sampling techniques can generate varying ANN performance on the same dataset.
Sebuah Karakteristik dari Modul Uniserial dan Gelanggang Uniserial

The module is a generalization of the vector space. The module that will be discussed is a uniserial module, which is a module that only has one composition series. A uniserial ring is a ring whose module over itself is uniserial. The uniserial ring is an Artin and local ring, but the converse is not necessarily true. In this paper, we will discuss Artin and local ring with additional properties so that it is characteristics of the uniserial ring.
Cloud Adoption in Enterprises

This chapter proposes a security and migration framework with business strategic implementation guidelines for successfully adopting cloud services, namely Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS) in contemporary organisations. As a foundation to achieve this, the authors give emphasis to the importance of considering the security, privacy, and governance issues related to cloud implementations, along with the possible benefits of adopting cloud services in businesses. They discuss the various types of cloud, their deployment models, and service levels as these form the basis for strategically planning the security and migration framework implementation of cloud in an organization. In addition, the authors provide a step-wise security and migration framework that could serve as a business strategic guideline for a successful cloud service adoption in enterprises.
Eye Blink Controlled Smart Wheelchair Aiding Disabled People

According to a new estimate from the organisation of world health more than 15 percent people in the world are disabled in some ways. The use of motorised wheelchairs with high navigational intelligence is one of the most significant advancements toward the integration of severely physically challenged and mentally disabled people. For seriously disabled person, accessing a wheelchair is found to be tough process unless they use their tongue to manipulate the joystick. Different technologies address many issues that disabled people come across, making people to move safely to complete some key everyday chores. In this paper, it shows a unique system that uses eye-blink sensors to allow a person with a motor handicap to drive a wheelchair. Signal transfer process is accomplished by the radio frequency transmission and reception. They can turn wheelchair left or right by simply staring in the desired direction, as well as start and stop it.