Reviewerly/exHarmony huggingface dataset:
View-independent Vehicle Category Classification System

Vehicle category classification is important, but it is a challenging task, especially, when the vehicles are captured from a surveillance camera with different view angles.This paper aims to develop a view-independent vehicle category classification system.It proposes a two-phase system: one phase recognizes the view angles helping the second phase to recognize the vehicle category including bus, car, motorcycle, and truck.In each phase, several descriptors and Machine Learning techniques including traditional algorithms and Deep neural networks are employed.In particular, we used three descriptors: HOG (Histogram of Oriented Gradient), LBP (Local Binary Patterns) and Gabor filter with two classifiers SVM (Support Vector Machine) and k-NN (k-Nearest Neighbor).And also, we used the Convolutional Neural Network (CNN, or ConvNet).Three experiments have been elaborated based on many datasets.The first experiment is dedicated to choosing the best approach for the recognition of views: rear or front.The second experiment aims to classify the vehicle categories based on each view.In the third experiment, we developed the overall system, the categories were classified independently of the view.Experimental results reveal that CNN gives the highest recognition accuracy of 94.29% in the first experiment, and HOG with SVM or k-NN gives the best results (99.58%, 99.17%) in the second experiment.The system can robustly recognize vehicle categories with an accuracy of 95.77%.
Peningkatan Kompetensi Guru Mengoperasional Microsoft Excel untuk Pengolahan Nilai Hasil Belajar di MI Negeri 8 Gunungkidul dan MI Muhammadiyah Munggur Semanu

This research aims to find out the ability of teachers to process the value of students' learning results using microsoft excel. Microsoft excel implementation activities are conducted through group supervision in the form of training and mentoring. This research uses a method of action research and is carried out in two cycles. The results showed that training and mentoring activities can improve teachers' ability to operate Microsoft Excel. In cycle I, of the 18 teachers, 83.33% had poor criteria, 11.11% had enough criteria, and 5.56% had a good criteria with an average score of 62.35 in cycle I increasing to 83.70 in cycle II. Group supervision activities through training and mentoring can improve the ability of MI teachers in processing the value of learning results using Microsoft excel.
Cross-Modal Progressive Comprehension for Referring Segmentation

Given a natural language expression and an image/video, the goal of referring segmentation is to produce the pixel-level masks of the entities described by the subject of the expression. Previous approaches tackle this problem by implicit feature interaction and fusion between visual and linguistic modalities in a one-stage manner. However, human tends to solve the referring problem in a progressive manner based on informative words in the expression, i.e., first roughly locating candidate entities and then distinguishing the target one. In this paper, we propose a Cross-Modal Progressive Comprehension (CMPC) scheme to effectively mimic human behaviors and implement it as a CMPC-I (Image) module and a CMPC-V (Video) module to improve referring image and video segmentation models. For image data, our CMPC-I module first employs entity and attribute words to perceive all the related entities that might be considered by the expression. Then, the relational words are adopted to highlight the target entity as well as suppress other irrelevant ones by spatial graph reasoning. For video data, our CMPC-V module further exploits action words based on CMPC-I to highlight the correct entity matched with the action cues by temporal graph reasoning. In addition to the CMPC, we also introduce a simple yet effective Text-Guided Feature Exchange (TGFE) module to integrate the reasoned multimodal features corresponding to different levels in the visual backbone under the guidance of textual information. In this way, multi-level features can communicate with each other and be mutually refined based on the textual context. Combining CMPC-I or CMPC-V with TGFE can form our image or video version referring segmentation frameworks and our frameworks achieve new state-of-the-art performances on four referring image segmentation benchmarks and three referring video segmentation benchmarks respectively.
Hindsight Trust Region Policy Optimization

Reinforcement Learning (RL) with sparse rewards is a major challenge. We pro- pose Hindsight Trust Region Policy Optimization (HTRPO), a new RL algorithm that extends the highly successful TRPO algorithm with hindsight to tackle the challenge of sparse rewards. Hindsight refers to the algorithm’s ability to learn from information across goals, including past goals not intended for the current task. We derive the hindsight form of TRPO, together with QKL, a quadratic approximation to the KL divergence constraint on the trust region. QKL reduces variance in KL divergence estimation and improves stability in policy updates. We show that HTRPO has similar convergence property as TRPO. We also present Hindsight Goal Filtering (HGF), which further improves the learning performance for suitable tasks. HTRPO has been evaluated on various sparse-reward tasks, including Atari games and simulated robot control. Experimental results show that HTRPO consistently outperforms TRPO, as well as HPG, a state-of-the-art policy 14 gradient algorithm for RL with sparse rewards.
Research on TFe Content of Hematite Based on LU-TELM-SOA and Selection of Band

Iron ore is an important raw material for the steel industry, so it is of great economic significance to determine the grade of the iron ore quickly and accurately. And the TFe content is the main indicator that determines the grade of the iron ore and whether the iron ore can be smelted directly. Unlike manual methods and methods for chemical analysis, the paper uses the selection of band for the near-infrared spectrum based on the pruning method and the two-hidden-layer extreme learning machine based on LU decomposition and seagull optimization algorithm (LU-TELM-SOA) to identify the TFe content. First of all, the paper proposes the selection of band based on the pruning method to retain the sensitive band of the near-infrared spectrum. Aiming at the problems of poor stability and low accuracy of a single LU-TELM (the two-hidden-layer extreme learning machine based on LU decomposition) model, the paper proposes LU-TELM-SOA. The experimental results show that LU-TELM-SOA has the advantages of high accuracy and strong stability.
Optimization of Code Parameters and Signal Processing Mode under Intentional Interference

We propose a universal method of video stegosystem detection, which was recently suggested by the authers. They describe the detecting algorithm and its efficiency is presented in terms of the missing and false detection probabilities. The article considers the possibility of the detection resistance of such stegosystems by the means of reducing the speed of embedding secret information in them.
CRFL: Certifiably Robust Federated Learning against Backdoor Attacks

Federated Learning (FL) as a distributed learning paradigm that aggregates information from diverse clients to train a shared global model, has demonstrated great success. However, malicious clients can perform poisoning attacks and model replacement to introduce backdoors into the trained global model. Although there have been intensive studies designing robust aggregation methods and empirical robust federated training protocols against backdoors, existing approaches lack robustness certification. This paper provides the first general framework, Certifiably Robust Federated Learning (CRFL), to train certifiably robust FL models against backdoors. Our method exploits clipping and smoothing on model parameters to control the global model smoothness, which yields a sample-wise robustness certification on backdoors with limited magnitude. Our certification also specifies the relation to federated learning parameters, such as poisoning ratio on instance level, number of attackers, and training iterations. Practically, we conduct comprehensive experiments across a range of federated datasets, and provide the first benchmark for certified robustness against backdoor attacks in federated learning. Our code is available at https://github.com/AI-secure/CRFL.
Digital Forensics Investigation Procedures of Smart Grid Environment

Smart grids have been widely used around the world.The security of this system is debatable among the researchers because this area requires an improvement in order to reassure the grid is secured from cyberattacks.However, many malware were found attacking the smart grid systems such as Stuxnet, Flames, Triton, etc.Some of them are designed to avoid being tracked by a forensic investigator.The perpetrators used the fragility of digital evidence as an advantage to launch an attack on the smart grid without leaving traces.Technology development gives challenges to digital forensic procedures because the data volume is much higher.Thus, the digital forensic procedure needs to be redesigned, modified, and improved to capture traces and handle digital evidence.This paper aims to propose a digital forensic procedure to guide investigators to perform the digital forensic investigation, especially in a smart grid environment.This paper has discussed several suitable tools and techniques in digital forensic investigation to solve the problem or the challenges.This study discussed two cyberattacks examples and simulated the attack using a testbed to guide forensic investigators based on the proposed digital forensic procedure.Examples of cyberattacks are Distributed Denial of Service and False Data Injection attacks.This paper presented an appropriate methodology and relevant forensic tools to ensure the evidence's integrity during collection and analysis as legal evidence in court.
Biclustering via Semiparametric Bayesian Inference

Motivated by classes of problems frequently found in the analysis of gene expression data, we propose a semiparametric Bayesian model to detect biclusters, that is, subsets of individuals sharing similar patterns over a set of conditions. Our approach is based on the well-known plaid model by Lazzeroni and Owen (2002). By assuming a truncated stick-breaking prior we also find the number of biclusters present in the data as part of the inference. Evidence from a simulation study shows that the model is capable of correctly detecting biclusters and performs well compared to some competing approaches. The flexibility of the proposed prior is demonstrated with applications to the analysis of gene expression data (continuous responses) and histone modifications data (count responses).
Food Reviews Classification using multi-label convolutional neural network text classifier

Most commercial websites, such as Amazon, encourage users to leave reviews of the goods and services they get after purchasing them. For certain consumers, this analysis is critical when determining whether or not to buy a product. Understanding the consequences of feedback and correctly classifying their utility may therefore be an advantageous method for websites. The classification results can also be used as a review and recommendation program for ongoing success. Nowadays people visit several restaurants on various occasions. They get confused most of the time after having a look at the food menu. Based on the ratings and reviews of the dish it becomes easier for them to decide the dish they wish to order. However they unable to read each review of the previous customers. So to overcome this issue, we have proposed NLP (Natural Language Processing) technique and Spacy CNN (Convolutional Neural Network) pipeline system which will classify all the reviews in a single rating. Each review is labelled with a reviewer's score indicating the sentiment of the reviewers. Our task is to predict a reviewer’s score on a scale of 0 or 1. Where 1 indicates the users like the dish while 0 indicates that the reviewers were not satisfied with the dish.
Human Activity Recognition: literature Review

Human activity recognition has an important role in the interaction between human and human relationships because it provides information about a person's identity, personality, activities, psychological state, and health, all this information is difficult to extract due to the difficulty of a person's ability to identify the activities of another person and is considered one of the basic research topics in the scientific fields in the field of computer vision and machine learning.the purpose of human activity recognition (HAR) is to identify the different human activities throw monitoring and register the human activates and the various surrounded environment, by using computers, the human activity recognition researches which depending on visions is the basics of lots of applications even video monitoring or health care and security monitoring and the interaction between the human and the computers.In this research, a review of the newest development in the human activity recognition branch have been studied, and the different ways to recognize the human actions, an important detail have been shown to preview the HAR researches and the methodologies used to represent the human activates and its classifications, to provide an overview of the HAR methods and comparing them.
SISTEM INFORMASI PENJUALAN DAN PERSEDIAAN PRODUK PERALATAN PERTANIAN BERBASIS WEB

The rapid development of technology has brought humans into a life side by side with information and technology itself. Nowadays, more and more companies need fast and accurate information services. Processing of sales and inventory data at the Growmart (Farmer Business Branch) Bagan Batu company which still uses the manual method makes recording sales data and printing reports slow and less efficient. To solve this problem, a web-based sales and inventory information system for agricultural equipment products was designed. This web-based information system was built using the PHP programming language and the Cascading Style Sheet (CSS) as content representation, and database processing using MySQL. The result of this research is an information system that helps the sales admin section of the Growmart company to input sales data and equipment product inventory easily, quickly and efficiently..Keywords: Farmer, sales, inventory, quickly, efficiently
An Efficient Algorithm for the VLSI Implementation of the Inverse DST Based on Quasi-Band Correlation Structures

This paper presents an efficient algorithm for the VLSI implementation of the Inverse Discrete Sine Transform (IDST) based on quasi-band correlation structures. The implementation uses a restructured form of the matrix-vector product from the IDST definition that is amenable to an efficient systolic array realization employing the quasi-band correlation type computational structure. The proposed algorithm exhibits the advantages of a low arithmetic complexity that leads to reduced hardware and I/O costs and improved high-speed performance.
Elisitasi Kebutuhan Untuk Meningkatkan Kepuasan Pengguna Pada Sistem E-Learning Universitas Muhammadiyah Malang

In recent years the use of information systems has changed very dramatically. Currently, many information systems are developed for various types of users. These various kinds of users have different characteristics, which makes it very difficult for organizations developing information systems to know the needs of their users. For this reason, a good and in-depth need elicitation is needed to really know the user's needs. The purpose of this study is to explore the needs of users in the E-Learning system of the University of Muhammadiyah Malang to increase user satisfaction of the system. In this study, several elicitation methods were used, namely interviews, storytelling, user personas, and storyboards. The interview method is carried out to explore the problems experienced by users when using the system. Then the results of the interview will be processed into a narrative form of storytelling and then poured into a persona document. By using the above method, you can dig deeper to get a sharper solution to the problems experienced by users. The results of these four methods have proven to be effective in the need elicitation process because these four methods are interrelated and complement each other. This study resulted in a storyboard containing recommended solutions from the results of the excavation of needs carried out on users of the University of Muhammadiyah Malang E-Learning system
DIEL: Interactive Visualization Beyond the Here and Now

Interactive visualization design and research have primarily focused on local data and synchronous events. However, for more complex use cases---e.g., remote database access and streaming data sources---developers must grapple with distributed data and asynchronous events. Currently, constructing these use cases is difficult and time-consuming; developers are forced to operationally program low-level details like asynchronous database querying and reactive event handling. This approach is in stark contrast to modern methods for browser-based interactive visualization, which feature high-level declarative specifications. In response, we present DIEL, a declarative framework that supports asynchronous events over distributed data. Like many declarative visualization languages, DIEL developers need only specify what data they want, rather than procedural steps for how to assemble it; uniquely, DIEL models asynchronous events (e.g., user interactions or server responses) as streams of data that are captured in event logs. To specify the state of a user interface at any time, developers author declarative queries over the data and event logs; DIEL compiles and optimizes a corresponding dataflow graph, and synthesizes necessary low-level distributed systems details. We demonstrate DIEL's performance and expressivity through ex-ample interactive visualizations that make diverse use of remote data and coordination of asynchronous events. We further evaluate DIEL's usability using the Cognitive Dimensions of Notations framework, revealing wins such as ease of change, and compromises such as premature commitments.
Enhancing Learners’ Vocabulary Acquisition by Crossword Puzzle Game

This study aimed at developing learners’ vocabulary acquisition by using a crossword puzzle game at an English course in Langsa, Aceh Province. It further explored how the technique helped the learners learned and acquired English vocabulary. This study was a Classroom Action Research (CAR) and it applied steps of action research set out by Kemmis and McTaggart, which encompasses planning, implementation, observation, and reflection. 17 learners were selected as the subject of this research purposively. Observation, interviews, and vocabulary tests were conducted to collect the data. The findings revealed that the use of crossword puzzle game developed the learners’ vocabulary acquisition effectively, as whole participants achieved the Minimum Mastery Criterion score (KKM), which was 75, successfully. The proportion of those who reach the score was 35,29% at the first cycle and the number was rose gradually to 100% at the third cycle. The study also found that the implementation of the crossword puzzle promoted an effective language-learning atmosphere. The learners engaged in the learning process and got involved in the activities enthusiastically. It developed the learners’ curiosity to complete the task and enhanced their self-confidence in learning English as they worked in groups. It also provided opportunities for the students to practice the vocabulary and to develop pronunciation.
Certified Defense via Latent Space Randomized Smoothing with Orthogonal Encoders

Randomized Smoothing (RS), being one of few provable defenses, has been showing great effectiveness and scalability in terms of defending against $\ell_2$-norm adversarial perturbations. However, the cost of MC sampling needed in RS for evaluation is high and computationally expensive. To address this issue, we investigate the possibility of performing randomized smoothing and establishing the robust certification in the latent space of a network, so that the overall dimensionality of tensors involved in computation could be drastically reduced. To this end, we propose Latent Space Randomized Smoothing. Another important aspect is that we use orthogonal modules, whose Lipschitz property is known for free by design, to propagate the certified radius estimated in the latent space back to the input space, providing valid certifiable regions for the test samples in the input space. Experiments on CIFAR10 and ImageNet show that our method achieves competitive certified robustness but with a significant improvement of efficiency during the test phase.
Themis: A Fair Evaluation Platform for Computer Vision Competitions

It has become increasingly thorny for computer vision competitions to preserve fairness when participants intentionally fine-tune their models against the test datasets to improve their performance. To mitigate such unfairness, competition organizers restrict the training and evaluation process of participants' models. However, such restrictions introduce massive computation overheads for organizers and potential intellectual property leakage for participants. Thus, we propose Themis, a framework that trains a noise generator jointly with organizers and participants to prevent intentional fine-tuning by protecting test datasets from surreptitious manual labeling. Specifically, with the carefully designed noise generator, Themis adds noise to perturb test sets without twisting the performance ranking of participants' models. We evaluate the validity of Themis with a wide spectrum of real-world models and datasets. Our experimental results show that Themis effectively enforces competition fairness by precluding manual labeling of test sets and preserving the performance ranking of participants' models.
Unsupervised Learning of Debiased Representations with Pseudo-Attributes

Dataset bias is a critical challenge in machine learning since it often leads to a negative impact on a model due to the unintended decision rules captured by spurious correlations. Although existing works often handle this issue based on human supervision, the availability of the proper annotations is impractical and even unrealistic. To better tackle the limitation, we propose a simple but effective unsupervised debiasing technique. Specifically, we first identify pseudo-attributes based on the results from clustering performed in the feature embedding space even without an explicit bias attribute supervision. Then, we employ a novel cluster-wise reweighting scheme to learn debiased representation; the proposed method prevents minority groups from being discounted for minimizing the overall loss, which is desirable for worst-case generalization. The extensive experiments demonstrate the outstanding performance of our approach on multiple standard benchmarks, even achieving the competitive accuracy to the supervised counterpart.