Reviewerly/exHarmony huggingface dataset:
Explainable Neural Network analysis on Movie Success Prediction

These days movies are one of the most important part of entertainment industry and back in the days you could see everyday people standing outside theatres, or watching movies in OTT platforms. But due to busy schedules not many people are watching every movie. They go over the internet and search for top rated movies and go to theatres. And creating a successful movie is no easy job. Thus, this study helps movie producers to consider what are the important factors that influence a movie to be successful. this study applied neural network model to the IMDb dataset and then due to its complex nature in order to achieve the local explainability and global explainability for the enhanced analysis, study have used SHAP (Shapley additive explanations) to analysis.
Fermionic adaptive sampling theory for variational quantum eigensolvers

Quantum chemistry has been identified as one of the most promising areas where quantum computing can have a tremendous impact. For current noisy intermediate-scale quantum (NISQ) devices, one of the best available methods to prepare approximate wave functions on quantum computers is the adaptive derivative-assembled pseudo-Trotter Ansatz variational quantum eigensolver (ADAPT-VQE). However, ADAPT-VQE suffers from a significant measurement overhead when estimating the importance of operators in the wave function. In this work, we propose fermionic adaptive sampling theory VQE (FAST-VQE), a method for selecting operators based on importance metrics solely derived from the populations of Slater determinants in the wave function. Thus, our method mitigates measurement overheads for ADAPT-VQE as it is only dependent on the populations of Slater determinants which can simply be determined by measurements in the computational basis. We introduce two heuristic importance metrics, one based on selected configuration interaction with perturbation theory and one based on approximate gradients. In state vector and finite shot simulations, FAST-VQE using the heuristic metric based on approximate gradients converges at the same rate or faster than ADAPT-VQE and requires dramatically fewer shots.
The Effect Of Crossword Puzzle Game On Students' Vocabulary Mastery At Grade Eight Of SMP Gkps 3 Pematang Siantar

The aim of this study is to find out whether crossword puzzle game is an effective media to improve students’ vocabulary mastery of descriptive text, and comparing with the students who were taught by using the conventional media. This study is a quantitative research using quasi-experimental as the method of collecting data. In this study, the data were taken from the pre-test and post-test. The Excel program was used to analyze the validity. The result shows that the mean of pre-test from the experimental group was 55,33, and the control group was 42,33. Then the mean of post-test from the experimental group was 82,5, and the score of the control group was 63,16 Based on the t-test calculation of post-test from both classes shows that the result of the t-value (3,666) was higher than the t-table (2.001). Since the t-test was higher than the t-table, it could be stated that there was a significant difference between the students’ achievement who were taught by using crossword puzzle game and those who were taught by using conventional learning media in improving vocabulary of descriptive text. The students who were taught by using crossword puzzle game have higher achievement. In other words, crossword puzzle game is effective to improve students’ vocabulary mastery. Based on this result, it is recommended that teachers use crossword puzzle in English classes because this game is very helpful for students in improving their vocabulary mastery.
A Novel Ensemble Model for Complex Entities Identification in Low Resource Language

The fundamental method for pre-processing speech or text data that enables computers to comprehend human language is known as natural language processing. Numerous models have been developed to date to pre-process data in the English language; however, the Hindi language does not support these models. India's national tongue is Hindi. In order to help the locals, the authors of this study used supervised learning methods like Linear Regression, SVM, and Naive Bayes algorithm to investigate a dataset of complicated terms in the Hindi language. Additionally, a sophisticated Hindi word classification model is suggested employing several methods based on the forecasts as well as collective learning strategies like Random Forest, Adaboost, and Decision Tree. Depending on how well the user's language is understood, the suggested model will assist in simplifying Hindi text. Authors attempt to classify the uncharted dataset using deep learning algorithms like Bi-LSTM and GRU approaches in further processing.
Effect of photonic errors on quantum enhanced dense-subgraph finding

We investigate the effects of photon loss and spectrally impure sources on Gaussian boson sampling (GBS) when used in dense-subgraph-finding algorithms. We find that the effectiveness of these algorithms is remarkably robust to such errors, to such an extent that there exist classical algorithms that can efficiently simulate the underlying GBS. These results suggest that, unlike the GBS problem itself, the speed-up of GBS-based algorithms over classical approaches when applied to the dense-subgraph problem is not exponential. They do suggest, however, that any advantage offered could be achieved on a quantum device with far less stringent requirements on photon loss and purity than general GBS.
Using Running Dictation Game To Improve Grade Ten Students' Understanding On Simple Present Tense In SMA Negeri 2 Pematang Siantar

This objective of this research is to find out the effect of using Running Dictation Game on Simple Present Tense SMA Negeri 2 Pematang Siantar in the academic year 2022/2023. Through the uses of a quasi-experimental design, this research uses a quantitative method. It has two classes, such as experimental class and control class which use in the research. 353 students in grade ten at SMA Negeri 2 Pematang Siantar make up the population of this research. Students from classes X-2 (experimental class) and X-1 (control class), who choose using the purposive sampling technique and make up the sample for this research. There are 10 multiple-choice questions and 10 essay on the research instrument, including a pre- and post-test that the researcher uses to assess the data. The researcher uses the t-test formula to assess the data. The result of this data shows that the mean score of pre-test in experimental class score is 74.5, while the mean post-test score is 92.7. In the control class, the pre-test mean is 63.2, and the post-test mean is 79.4. After calculating the mean score for both groups and using the t-test formula, the results show that the t-test has the highest significance level in the t-table with 5% level significance (1.807&gt; 1,667). So, using running dictation game to improve grade ten has significant effect to the students’.
Image-centric compression of protein structures improves space savings

Abstract Background Because of the rapid generation of data, the study of compression algorithms to reduce storage and transmission costs is important to bioinformaticians. Much of the focus has been on sequence data, including both genomes and protein amino acid sequences stored in FASTA files. Current standard practice is to use an ordinary lossless compressor such as gzip on a sequential list of atomic coordinates, but this approach expends bits on saving an arbitrary ordering of atoms, and it also prevents reordering the atoms for compressibility. The standard MMTF and BCIF file formats extend this approach with custom encoding of the coordinates. However, the brand new Foldcomp tool introduces a new paradigm of compressing local angles, to great effect. In this article, we explore a different paradigm, showing for the first time that image-based compression using global angles can also significantly improve compression ratios. To this end, we implement a prototype compressor ‘PIC’, specialized for point clouds of atom coordinates contained in PDB and mmCIF files. PIC maps the 3D data to a 2D 8-bit greyscale image and leverages the well developed PNG image compressor to minimize the size of the resulting image, forming the compressed file. Results PIC outperforms gzip in terms of compression ratio on proteins over 20,000 atoms in size, with a savings over gzip of up to 37.4% on the proteins compressed. In addition, PIC’s compression ratio increases with protein size. Conclusion Image-centric compression as demonstrated by our prototype PIC provides a potential means of constructing 3D structure-aware protein compression software, though future work would be necessary to make this practical.
The Effect Of Cooperative Integrated Reading And Composition (Circ) Technique On Reading Comprehension Of Narrative Text At Grade Eighth Students Of SMP Swasta GKPS 3 Pematang Siantar

This research aimed to find Out The Effect Of Cooperative Integrated Reading And Composition (Circ) Technique On Reading Comprehension Of Narrative Text At Grade Eighth Students Of Smp Swasta Gkps 3 Pematang Siantar. This research design is quantitative. The quantitative method is a planned and careful problems solving method. In quantitative, systematically control data collection design, and will focus on constructing inductively concluded theories within the framework of empirically proving hypothesis. The quantitative research imposes objective measuring to collect numeric data that are utilized the answers of questions or test the specified hypothesis. Researcher will choose two groups as the sample, namely class VIII.1 and VIII.2. The two classes that researcher will choose as sample have studied the narrative text based on school education curriculum. By calculating the data, we can conclude that the t-test result (2.42) exceeded the t-table value at a significance level of 0.05 (2.00), meaning 2.42 &gt; 2.00. This indicates that the use of the CIRC technique has a significant impact on students' reading comprehension. This is evident from the list of student scores in reading comprehension when using the CIRC technique compared to the conventional technique. In other words, grade eighth students' capacity at SMP Swasta GKPS 3 Pematang Siantar in reading comprehension is significantly impacted by using of CIRC technique.
Poster: Unveiling the Impact of Patch Placement: Adversarial Patch Attacks on Monocular Depth Estimation

For autonomous driving systems, cameras and LiDAR sensors are necessary devices that provide precise depth information by which positions and sizes of objects can be identified. Moreover, recent advances in deep learning have extended their capabilities to include monocular camera setup for depth estimation. Compared with the conventional devices like LiDAR or stereo cameras for the depth estimation, the monocular camera enables to estimate depths with a low cost. It is known that the depth estimation models for the monocular camera are vulnerable to adversarial examples. However, most adversarial attacks against the monocular depth estimation have been conducted with targeted patches that are placed on a target object. It is known that the targeted patch outperforms the adjacent and remote patch that is placed beyond the target object, when it comes to an attack success rate. However, the adjacent and remote patch would provide high flexibility in patch placement, as it can be placed beyond the target object's scope. In this paper, we experimentally confirm that the patch placement significantly affects the attack success rates, particularly in specific regions.
A Good Fishman Knows All the Angles: A Critical Evaluation of Google's Phishing Page Classifier

Phishing is one of the most popular cyberspace attacks. Phishing detection has been integrated into mainstream browsers to provide online protection. The phishing detector of Google Chrome reports millions of phishing attacks per week. However, it has been proven to be vulnerable to evasion attacks. Currently, Google has upgraded Chrome/Chromium's phishing detector, introducing a CNN-based image classifier. The robustness of the new-generation detector is unclear. If it can be bypassed, its billions of users will be exposed to sophisticated attackers. This paper presents a critical evaluation of Google's phishing detector by targeted evasion testing, and investigates corresponding defensive techniques. First, we propose a three-stage evasion method against the phishing image classifier. The experiments show that it can be completely bypassed with adversarial phishing pages generated using the proposed method. Meanwhile, the phishing pages still preserve their visual utility. Second, we introduce two defense techniques to enhance the phishing detection model. The results show that even using lightweight defense methods can significantly improve the model robustness. Our research reveals that Google's new-generation phishing classifier is very vulnerable to targeted evasion attacks. A sophisticated phishers can know how to fool the classifier. Billions of Chrome users are being exposed to potential phishing attacks. To improve its robustness, necessary security enhancements should be introduced.
CCSW '23: Cloud Computing Security Workshop

Clouds and massive-scale computing infrastructures are starting to dominate computing and will likely continue to do so for the foreseeable future. Major cloud operators are now comprising millions of cores hosting substantial fractions of corporate and government IT infrastructure. CCSW is the world's premier forum bringing together researchers and practitioners in all security aspects of cloud-centric and outsourced computing, including:
Realizing Flexible Broadcast Encryption: How to Broadcast to a Public-Key Directory

Suppose a user wants to broadcast an encrypted message to K recipients. With public-key encryption, the sender would construct K different ciphertexts, one for each recipient. The size of the broadcasted message then scales linearly with K. A natural question is whether the sender can encrypt the message with a ciphertext whose size scales \em sublinearly with the number of recipients.
Demo: Data Minimization and Informed Consent in Administrative Forms

This article proposes a demonstration implementing the data minimization privacy principle, focusing on reducing data collected by government administrations through forms. Data minimization is defined in many privacy regulations worldwide, but has not seen extensive real-world application. We propose a model based on logic and game theory and show that it is possible to create a practical and efficient solution for a real French welfare benefit case.
WPES '23: 22nd Workshop on Privacy in the Electronic Society

These proceedings contain the papers selected for inclusion in the technical program for the 22st ACM Workshop on Privacy in the Electronic Society (ACM WPES 2023), held in conjunction with the 30th ACM Conference on Computer and Communication Security (ACM CCS 2023) at the Tivoli Congress Center in Copenhagen, Denmark, on November 26, 2023. In response to the workshop's call for papers, 31 valid submissions were received, including 21 full paper submissions and 10 short paper submissions. They were evaluated by a technical program committee consisting of 54 researchers whose backgrounds include a diverse set of topics related to privacy. Each paper was reviewed by at least 3 members of the program committee. Papers were evaluated based on their importance, novelty, and technical quality. After the rigorous review process, 9 submissions were accepted as full papers (acceptance rate: 29.0%) and an additional 8 submissions were accepted as short papers.
Poster: Backdoor Attack on Extreme Learning Machines

Deep neural networks (DNNs) achieve top performance through costly training on large datasets. Such resources may not be available in some scenarios, like IoT or healthcare. Extreme learning machines (ELMs) aim to alleviate this problem using single-layered networks, requiring fewer training resources. Current investigations have found that DNNs are prone to security and privacy threats, where malfunction of the network or training data extraction can be performed.
Lung cancer histopathology image classification using transfer learning with convolution neural network model

BACKGROUND: Lung cancer (LC) is a harmful malignant tumor and potentially lethal illness. Therefore, early detection of LC is an urgent need, and dependent on the type of histology and the type of disease. The use of deep learning algorithms (DL) is required to analyse the histopathology images of LC and make treatment decisions accordingly. OBJECTIVE: This study aimed to apply pretrained EfficientNetB7 model to facilitate the process of classifying LC histopathology images as primary malignancy categories (adenocarcinoma, squamous cell carcinoma and large cell carcinoma) for early treatment of LC patients. Also, aims to analyse the performance of the proposed model using the accuracy measure. METHODS: The dataset of 15000 histopathology images of lung cancer were examined. EfficientNetB7, a special type of convolution neural network (CNN), pretrained with ImageNet for transfer learning were trained on this dataset. Accuracy metric was used for the evaluation of the proposed model. RESULTS: The feature extraction was performed by applying transfer learning using EfficientNetB7 as pretrained model. The proposed model achieved 99.77% accuracy, while previous studies model achieved over 90 to 99% accuracy. CONCLUSION: The employment of CNN based EfficientNetB7 model for the classification of LC based on histopathology images can speed up the diagnosis of LC and reduce the burden on pathologists for the early treatment of patients.
Fake face detection based on a multi discriminator deep CNN architecture (MDD-CNN)

Due to the robustness of the deep learning tools used to design these applications, fakes are becoming increasingly common as these applications become more widely available and accessible to the general public. These fakes are typically fake faces or even fake people, which are difficult to distinguish from real individuals. Therefore, we need more efficient applications for fraud detection. In this work, we propose a new multi-discriminator architecture to distinguish fake faces from real ones. The architecture consists of three deep networks (discriminators) competing with each other, each trained differently. The final decision is made by voting based on the decisions of the three discriminators. The core element of our architecture is the proposed new adversarial deep network discriminator (NDGAN), which is trained in three different ways, resulting in three distinct discriminators. Discriminator 1 undergoes adversarial training, discriminator 2 is trained using transfer learning, and the third discriminator undergoes supervised training with a standard CNN using examples and counterexamples. Training and testing were performed on 70 000 real faces from the Flickr-Face-HQ (FFHQ) dataset, while 70 000 fake faces were generated using Nvidia’s StyleGAN. The tests conducted on the three networks produced significant results, with accuracy ranging from 79 % to 98 % for fake faces, and from 80 % to 98 % for real faces. The reliability of the discriminators contributes significantly to the overall performance of the multi-discriminator system, achieving an accuracy of 96 % for fake faces and 98 % for real faces.
Developing Students’ Speaking Skill through Small Group Discussion (SGD) Technique

The purpose of this research is to find out whether or not the use of Small Group Discussion (SGD) technique can develop speaking skill to the tenth grade students of SMA Negeri 1 Bolano Parigi Moutong District. This research used a quasi-experimental research design with 77 students as a sample selected by using the cluster sampling technique. Additionally, the data of this research was obtained through a test which was divided into pre-test and post-test. The mean pre-test score for the experimental group and the control group were 48.90 and 55.77. The mean post-test score for the experimental group and the control group were 73.90 and 60.04. The data was theoretically analyzed by applying the t-test formula where the degree of freedom (df) is 75. The result of statistical analysis shows that the t-test value is 3.19 while the t-table value is 1.99. The result of the research findings shows that the t-test value is higher than the t-table value. It implies that the research hypothesis is accepted. It means that the use of Small Group Discussion (SGD) technique can develop speaking skill to the tenth grade students of SMA Negeri 1 Bolano Parigi Moutong District.
Pengembangan Parsing PCPATR Sebagai Preservasi Bahasa dan Sastra Banjar

Banjar Mantra is part of the Banjar language and literature in the form of words recited orally by certain people. Mantras are considered to have magical powers for supernatural powers and curses. This study aims to develop a sentence parsing device as a Language and Literature Preservation of Banjar using a sentence parsing application (parse PC-PATR). Parse PC-PATR is a sentence parsing application that will produce a pattern tree to represent the syntactic structure of sentences based on the Banjar language grammars. The problem in this research is that the mantra corpus uses the old genre of Banjarese language and literature. Words in the old genre are currently not widely used in communication by the Banjar people. On the other hand, the absorption of Arabic also presents its own problems. Finally, it is difficult to find the availability of experts who still preserve the Banjar mantra. This research used the ADDIE method with 20 Banjar mantras as test sentences and involved 3 committees of linguists as examiners. The research results showed that the naturalness of the test sentences was as low as 6%. The average parsing tree suitability percentage is 98%. This shows that the sentence parser is acceptable even though the naturalness of the test sentences is only 18% on average.
MEANS AND METHODS OF PEDAGOGICAL CONTROL OF A TUTOR AND A SCHOOL TEACHER

One of the ways to improve the quality of education is the differentiated approach in teaching. Data on learning outcomes can be one of the criteria for differentiation. The article analyses the means of pedagogical control used by tutors and school teachers and the level of using the data obtained during this control for the educational process management. The conducted research allowed the article authors to identify functions, methods and purposes that are of priority in pedagogical control. The results obtained in the course of the study can be regarded as premises for further tutors’ experience research, in order to identify pedagogical conditions for the implementation of differentiated learning in individual lessons with senior schoolchildren.