Reviewerly/exHarmony huggingface dataset:
A Tamper-Resistant Algorithm Using Blockchain for the Digital Tachograph

A tachograph in a vehicle records the vehicle operating conditions, such as speed, distance, brake operation conditions, acceleration, GPS information, etc., in intervals of one second. For accidents, the tachograph records information, such as the acceleration and direction of a vehicle traveling in intervals of 1/100 s for 10 s before and after the accident occurs as collision data. A vehicle equipped with a tachograph is obliged to upload operation data to administrative organizations periodically via other auxiliary storage devices like a USB attached external memory or online wireless communication. If there is a problem with the recorded contents, data may be at risk of being tampered with during the uploading process. This research proposed tamper-resistant technology based on blockchain for data in online and offline environments. The suggested algorithm proposed a new data recording mechanism that operates in low-level hardware of digital tachographs for tamper-resistance in light blockchains and on/offline situations. The average encoding time of the proposed light blockchain was 1.85 ms/Mb, while the average decoding time was 1.65 ms/Mb. With the outliers in statistical tests removed, the estimated average encoding and decoding time was 1.32 ms/Mb and 1.29 ms/Mb, respectively, and the tamper verification test detected all the tampered data.
Grading of Brain Histopathology Images via Convolutional Neural Networks

Grading is the process of determining the aggressiveness of tumors. Correct grading of histopathology images of the brain is very important for treatment planning. Pathologists examine the tissues with a microscope and decide which stage the brain tumors belong to. This process is time consuming and expert experience is important. With the advances in computer technology, computer aided image analysis on histopathological tissues has become possible. In this study, the AlexNet-based Convolutional Neural Network model was evaluated for automatic grading of astrocytomas using brain histopathology images. According to the simulation results, the proposed model reached 86.3% accuracy in the classification of all phases, while the low level histological images reached 98.38% accuracy.
Geometry and kinematics of brittle deformation in the Central Cameroon Shear Zone (Kékem area): Implication for gold exploration within the Central Africa Fold Belt in Cameroon

The Central Africa Fold Belt (CAFB) is a collision belt endowed with gold deposits in Eastern Cameroon area mined for about 50 years. However, favorable areas for gold exploration are poorly known. This paper presents (1) the kinematics of the brittle deformation in the Kékem area in the SW portion of the Central Cameroon Shear Zone and (2) constraints gold mineralization events with respect to the collisional evolution of the CAFB. The authors interpret that the conjugate ENE to E and NNW to NW trending lineament corresponds to the synthetic (R) and the antithetic (R’) shears, which accompanied the dextral slip along the NE to ENE striking shear. The latter coincides with the last 570–552 Ma D<sub>3</sub> dextral simple shear-dominated transpression, which is parallel to the Bétaré Oya shear zone hosting gold deposits. Gold mineralizations, which mainly occurred during the last dextral shearing, are disseminated within quartz veins associated to Riedel’s previous structures reactivated due to late collisional activities of the CAFB as brittle deformation. Gold mineralizations occurred mainly during the 570–552 Ma D<sub>3</sub> event. The reactivation, which might be due to dextral simple shear during mylonitzation, plausibly remobilized the early gold deposits hosted in syn-compressional rocks and/or possibly focused deep-sourced fluid mixed with those released by dehydration. Therefore, the Central Cameroon Shear Zone where Kékem is located, and which shows similar petrographical and structural features to those controling Batouri gold district, is a target area for gold exploration in Cameroon.
An overview of Gianni Rodari's books in translation around the world (2000-2020)

As the fame of Gianni Rodari reached a new height in 2020 with the celebration of his centennial, this overview of his legacy focuses on the most recent publication of Rodari’s works in translation around the world beginning in 2000. His versatility as a writer for children is evident in his entire body of works, his poetry, and his prose. Rodari’s translations hardly reflect on the diversity of the genres he had employed in his writings. The outcome of my investigation is the following: Rodari’s playfulness in writing, especially in poetry, has found recognition outside of Italy only in Russia. Other countries seem to prefer Rodari’s prose — his short stories or novels, especially his earlier works such as Cipollino and Telephone Tales. More research has to be done to keep Rodari’s popularity thriving, but, as these preliminary findings demonstrate, a renewed interest in the literary legacy of this beloved Italian children’s writer is widely spread nowadays, thus promising new poetic translations.
Rip van Winkle's Razor: A Simple Estimate of Overfit to Test Data

Traditional statistics forbids use of test data (a.k.a. holdout data) during training. Dwork et al. 2015 pointed out that current practices in machine learning, whereby researchers build upon each other's models, copying hyperparameters and even computer code -- amounts to implicitly training on the test set. Thus error rate on test data may not reflect the true population error. This observation initiated {\em adaptive data analysis}, which provides evaluation mechanisms with guaranteed upper bounds on this difference. With statistical query (i.e. test accuracy) feedbacks, the best upper bound is fairly pessimistic: the deviation can hit a practically vacuous value if the number of models tested is quadratic in the size of the test set. In this work, we present a simple new estimate, {\em Rip van Winkle's Razor}. It relies upon a new notion of \textquotedblleft information content\textquotedblright\ of a model: the amount of information that would have to be provided to an expert referee who is intimately familiar with the field and relevant science/math, and who has been just been woken up after falling asleep at the moment of the creation of the test data (like \textquotedblleft Rip van Winkle\textquotedblright\ of the famous fairy tale). This notion of information content is used to provide an estimate of the above deviation which is shown to be non-vacuous in many modern settings.
Robot Path Planning Algorithm Based on Particle Swarm Optimization and Feedforward Neural Network in Network Environment

The main task of mobile robot path planning is: according to the environment model, the mobile robot is based on one or some optimization criteria: such as the minimum work cost, the shortest walking route, the shortest walking time, etc., to find a path in the motion space that does not occur with obstacles. Under the premise of collision, the collision-free path from the starting coordinate point to the target coordinate point allows the robot to reach the destination safely. At present, the path planning methods of mobile robots can be roughly divided into three categories according to their working methods. The first is path planning based on environmental models. It can handle path planning under the condition of fully known obstacle positions and shapes. In the environment, the path planning method based on the environment model will not be applicable. Specific methods such as A * [1], topological graph method [2], etc .; second is local path planning method based on sensor information, typical methods are: artificial potential field method [3], fuzzy logic method [4], etc .; third Behavior-based path planning method [5], which decomposes the navigation problem into independent modules such as collision avoidance and target guidance [6]. Practice shows that it is an effective method to apply neural network to automatic generation of robot trajectory and path planning of mobile robot.
Identification of Boosters as Metadiscourse across Punjabi and Urdu Languages: A Machine Translation Approach

Boosters are said to function appropriately as metadiscourse features across languages. This study, therefore, aimed to investigate the functions and appropriateness of the metadiscourse features across Punjabi and Urdu languages. For this purpose, a list of 79 boosters (as metadiscourse features) was considered that (boosters) were first transliterated across Punjabi and Urdu languages employing a machine translation process. Punjabi translation was carried through ‘Akhar’ (a software), and Punjabi corpus (a tool). Whereas Urdu translation was realized through online Urdu thesaurus, and ‘ijunoon’ (an online dictionary). Machine transliteration was followed by manual cleansing of Punjabi and Urdu translated wordlists that helped identify boosters in the corpora. Appropriateness of the identified boosters was then realized through expert opinion and Punjabi corpus (for the Punjabi language), and expert opinion, online Urdu thesaurus, and Urdu WordNet (for the Urdu language). This process further guided about how to; make wordlists, filter as well as verify translated words, and offer interactional and interactive metadiscourse categories across Punjabi and Urdu languages.
PENGARUH POLA ASUH ORANG TUA TERHADAP KEDISIPLINAN BELAJAR PESERTA DIDIK

Parent's attention to children is still lacking, caused by several things including busyness, low educational background, lack of parent's direction of everything that has been achieved by the child, causing the child to become indifferent to learning. The problems that arise are how the influence of parenting parents on student learning discipline at MTs Al Hidayah Ciomas Panjalu.This study aims to determine the extent of the influence of parenting on student learning discipline in MTs Al Hidayah Ciomas Panjalu.The method used in this research is descriptive method with quantitative approach. The study population at MTs Al Hidayah was 162 students with a sample of 35 people. Sampling was done by purposive sampling technique. The data collection tool taken was observation and questionnaire as many as 35 respondents, after the data was collected with complete stages. The next step was data analysis using spermant rank coefficient (rs). Parental parenting values in Al Hidayah MTs were obtained at 39.4 at intervals of 38.2 - 40.3 with a fairly good classification. While the learning discipline value of students in Al Hidayah MTs was obtained at 37.97 at intervals of 35.22 - 38.44 with poor classification. Based on the testing of hypotheses that have been done, the results have a pretty good effect between parenting parents with student learning discipline in MTs Al Hidayah. This is obtained from rs = 0.59 with the Guilford scale at an interval of 0.41 - 0.60 with a fairly good classification. The pattern of Oreangtua Parenting affects the Discipline of Student Learning is 35% so 65% is influenced by other factors. The influence of these two variables, namely Parenting Parents Against Student Learning Discipline is a significant correlation. This is evidenced by t_count = 4.20&gt; t_table = 1.69236 so that Ha is accepted and Ho is rejected. Thus Parenting Parents have a pretty good effect on Student Learning Discipline.
MENYINGKAP BUDAYA KECURANGAN DALAM ORGANISASI KEMAHASISWAAN

fund activities. This research is a qualitative descriptive study using ethnographic methods. The data in this study are primary data obtained through in-depth interviews with committee members and functionaries of Student Institutions at X University. The results of the research show that the culture of fraud that exists in the management of student organization fund activities is a culture of 'herding' and culture 'everything can be arranged '. In this case, the 'herding' culture inherent in the committee was triggered by rationalization, so that the perpetrators of fraud felt that what was done was right. Moreover, supported by information related to fraud that has been committed by the previous committee without getting sanctions. While the culture of 'everything can be arranged' is also triggered by the opportunity and the cooperation in a committee that makes it easier for them to launch an act of fraud. Keywords: Culture , fraud , student organizations
ANALISIS KESALAHAN BERBAHASA INDONESIA DALAM TATARAN SINTAKSIS PADA TEKS PIDATO SISWA KELAS X PERAWAT 1 SMK KESEHATAN NUSANTARA SURABAYA

Language as the most important communication tool in human life. Humans can continue to live well and in order with language. Without language everyone will find it difficult to express opinions, ideas, or ideas that they are thinking about. Starting from this, researchers felt interested in examining the speech text of class X Nurse 1 at Nusantara Health Vocational School Surabaya in the syntactic level. The method used in this research is descriptive qualitative method. While the data collection methods in this study include (1) the listening technique, and (2) the note taking technique. While the data analysis in this study is by the method of covering (1) the basic technique of the BUL technique, (2) the advanced technique of fade technique, the dressing technique. The results of this study yaity (1) Language errors in the structure of sentences there are 10 data language errors in sentence structure divided into subject errors (S) of 4 errors, predicate (P) 4 errors, subject and predicate (SP) 1 error, and subjects, predicate, complement (SP Pel) 1 error in the speech text of class X Nurse 1 of SMK Health Nusantara Surabaya, (2) Language errors in the use of particle accuracy there is only 1 error of particle accuracy in speech text of class X Nurse 1 of SMK Health Nusantara Surabaya, including: particle accuracy "even", (3) Language errors in word selection there are 62 word selection errors in the speech text of class X Nurse 1 Vocational School Nusantara Health Surabaya, including: (a) word accuracy errors there are 8 errors, (b) errors word stiffness there are 34 errors divided into 22 word stiffness errors and 12 word stiffness errors, (c) word generosity errors there are 7 errors, (d) errors of word saving there are 9 errors, and (e) errors of subtlety of the meaning of words there are 4 errors.
Shift Invariance Can Reduce Adversarial Robustness

Shift invariance is a critical property of CNNs that improves performance on classification. However, we show that invariance to circular shifts can also lead to greater sensitivity to adversarial attacks. We first characterize the margin between classes when a shift-invariant linear classifier is used. We show that the margin can only depend on the DC component of the signals. Then, using results about infinitely wide networks, we show that in some simple cases, fully connected and shift-invariant neural networks produce linear decision boundaries. Using this, we prove that shift invariance in neural networks produces adversarial examples for the simple case of two classes, each consisting of a single image with a black or white dot on a gray background. This is more than a curiosity; we show empirically that with real datasets and realistic architectures, shift invariance reduces adversarial robustness. Finally, we describe initial experiments using synthetic data to probe the source of this connection.
Education About Anemia And Nutrition In Adolescent Girls

The increase in nutritional needs in adolescence is related to the acceleration of growth, where the nutrients that enter the body are used to increase body weight and height accompanied by an increase in the number and size of body cell tissues. in daily food consumption. Young women generally experience deficiencies of iron, calcium, and vitamin A. In addition, there are also deficiencies of vitamin B6, zinc, folic acid, iodine, vitamin D, and magnesium. One of the four nutritional problems being faced by developing countries, including Indonesia, is the problem of iron nutrition anemia. Teenage girls are among the vulnerable to suffer from anemia because young women are in their infancy and have menstruation every month which causes iron loss.The method of implementing this community service activity includes socialization to the Chairperson of the Foundation at the Puteri 'Aisyah Muhammadiyah Orphanage, Banjarmasin City for the implementation of community service activities regarding anemia prevention education in adolescents. Implementation of community service takes place according to the plan that has been made. The material was delivered and the discussion process took place during the education provision. Teens can understand the prevention of anemia in adolescents.
A (k, n) multi-secret image sharing scheme based on Chinese remainder theorem and Arnold cat map

A multi-secret image sharing (MSIS) scheme is a procedure to break down secret images into numerous shares and distribute each share to each authorized participant. Most existing ( n , n ) schemes face the problem of all-or-nothing that is all the n shares are required to reconstruct a secret image. If a single share is lost, then the secret image will not be recovered. Moreover, the existing ( k , n ) MSIS schemes either require k consecutive shares or general access structure to recover the secret images. The proposed scheme addresses the previous schemes’ issues. It is based on Arnold cat map, Chinese remainder theorem (CRT), and Boolean operation. It is a ( k , n ) threshold scheme where k is the threshold, and n is the number of participants. Arnold cat map is adopted for the randomization of images, and Boolean operation is used for producing public share images. CRT is utilized for the recovery of images. It has high fault tolerance capability as any of the k participants can submit their shares to reconstruct images and consume less computational time due to employing the modular method. Moreover, it can withstand differential as well as statistical attacks.
Incorporating VAD into ASR System by Multi-task Learning

When we use End-to-end automatic speech recognition (E2E-ASR) system for real-world applications, a voice activity detection (VAD) system is usually needed to improve the performance and to reduce the computational cost by discarding non-speech parts in the audio. Usually ASR and VAD systems are trained and utilized independently to each other. In this paper, we present a novel multi-task learning (MTL) framework that incorporates VAD into the ASR system. The proposed system learns ASR and VAD jointly in the training stage. With the assistance of VAD, the ASR performance improves as its connectionist temporal classification (CTC) loss function can leverage the VAD alignment information. In the inference stage, the proposed system removes non-speech parts at low computational cost and recognizes speech parts with high robustness. Experimental results on segmented speech data show that by utilizing VAD information, the proposed method outperforms the baseline ASR system on both English and Chinese datasets. On unsegmented speech data, we find that the system outperforms the ASR systems that build an extra GMM-based or DNN-based voice activity detector.
Experiments with Rich Regime Training for Deep Learning

In spite of advances in understanding lazy training, recent work attributes the practical success of deep learning to the rich regime with complex inductive bias. In this paper, we study rich regime training empirically with benchmark datasets, and find that while most parameters are lazy, there is always a small number of active parameters which change quite a bit during training. We show that re-initializing (resetting to their initial random values) the active parameters leads to worse generalization. Further, we show that most of the active parameters are in the bottom layers, close to the input, especially as the networks become wider. Based on such observations, we study static Layer-Wise Sparse (LWS) SGD, which only updates some subsets of layers. We find that only updating the top and bottom layers have good generalization and, as expected, only updating the top layers yields a fast algorithm. Inspired by this, we investigate probabilistic LWS-SGD, which mostly updates the top layers and occasionally updates the full network. We show that probabilistic LWS-SGD matches the generalization performance of vanilla SGD and the back-propagation time can be 2-5 times more efficient.
Practical Encrypted Computing for IoT Clients

Privacy and energy are primary concerns for sensor devices that offload compute to a potentially untrusted edge server or cloud. Homomorphic Encryption (HE) enables offload processing of encrypted data. HE offload processing retains data privacy, but is limited by the need for frequent communication between the client device and the offload server. Existing client-aided encrypted computing systems are optimized for performance on the offload server, failing to sufficiently address client costs, and precluding HE offload for low-resource (e.g., IoT) devices. We introduce Client-aided HE for Opaque Compute Offloading (CHOCO), a client-optimized system for encrypted offload processing. CHOCO introduces rotational redundancy, an algorithmic optimization to minimize computing and communication costs. We design Client-Aided HE for Opaque Compute Offloading Through Accelerated Cryptographic Operations (CHOCO-TACO), a comprehensive architectural accelerator for client-side cryptographic operations that eliminates most of their time and energy costs. Our evaluation shows that CHOCO makes client-aided HE offloading feasible for resource-constrained clients. Compared to existing encrypted computing solutions, CHOCO reduces communication cost by up to 2948x. With hardware support, client-side encryption/decryption is faster by 1094x and uses 648x less energy. In our end-to-end implementation of a large-scale DNN (VGG16), CHOCO uses 37% less energy than local (unencrypted) computation.
Machine Learning for Computer Music Multidisciplinary Research: A Practical Case Study

This paper presents a multidisciplinary case study of practice with machine learning for computer music. It builds on the scientific study of two machine learning models respectively developed for data-driven sound synthesis and interactive exploration. It details how the learning capabilities of the two models were leveraged to design and implement a musical interface focused on embodied musical interaction. It then describes how this interface was employed and applied to the composition and performance of ægo, an improvisational piece with interactive sound and image for one performer. We discuss the outputs of our research and creation process, and expose our personal reflections and insights on transdisciplinary research opportunities framed by machine learning for computer music.