Reviewerly/exHarmony huggingface dataset:
Digital competence as one of the components of the professional training of a PD inspector

Today, many researchers note the trend of digitalization, which represents the introduction of innovative technologies in all spheres of public life, while law enforcement is no exception. An absolute novelty of the current time has become the need for the interaction of the PDN inspector with the digital space, which is justified primarily by the increase in the activity of minors in the information space. In modern realities, the PDN inspector must be competent in matters of the digital space, moreover, the digital competence of an employee can help improve the efficiency of professional activities in general. The article substantiates the importance of the formation of digital competence in the PDN inspector, as one of the components of professional training. The subject of the study is digital competence as an integral element of the professional activity of a PDN inspector. &#x0D; The purpose of the study is to substantiate the importance of the digital competence of the PD inspector, to determine the components of digital competence, as well as to identify the main components of the formation of digital competence. &#x0D; Based on the results of the study, it was revealed that the formation of digital competence even at the stage of professional training of a PDN inspector allows a specialist to rationally interact with innovative products and apply digital technologies in the process of performing job duties, thus increasing the efficiency and effectiveness of professional activities in general.
Rotated Object Detector with Self-Attention and Improved IoU Loss

Nowadays in UAV and remote sensing image processing area, rotated object detection has been attached more and more attentions. However, there are few studies of this based on Transformer, which is much stronger than CNN for feature extraction. Moreover, the well-developed IoU based loss function in horizontal object detection area is not well fits with rotated objects. In this paper, we argue that Transformer of pyramid structure called Swin-Transformer is an effective alternative of CNN. Specifically, the finetuned Swin-Transformer with Relatively Position Encoding (RPE) performed much better than other backbones generally used. Moreover, the new kind of IoU loss called Gaussian Estimate Loss (GE Loss) that use gaussian kernel to model object is applied in our model. It can increase the precious of the model. This loss is in contract to other Gaussian modeling loss function for adding direction vector that can solve the difficulty of testing objects close to square. Experiments on DOTA dataset achieved 84.99% map, which indicates that and our experiment shows that these improvements of our model are effective.
PENGGUNAAN METODE SYNTHETIC STRUCTURAL ANALYTICAL (SAS) UNTUK MENINGKATKAN KAPASITAS MEMBACA AWAL PADA SISWA KELAS 1 SD

Through the use of Synthetic Analytical Structural (SAS) methods, this research seeks to enhance the implementation of the first reading learn and improve processes. the outcomes of using Synthetic Analytic Structure (SAS) Methods to enhance beginners' reading abilities SDN Cikalong Class I Students 2. Synthetic analytic structure (SAS) methods are used to improve the reading process and initial learning outcomes. This is done through classroom activities and qualitative research. Collaboration between teachers and students for classroom action research. The findings demonstrated that the use of Synthetic Analytical Structural Methods (SAS) to create wholesome, spotless, and lovely Ambient materials could enhance students' beginning reading abilities. It can be inferred from pre-cycle observations that the level of learning outcomes for reading in a tidy, healthy, and attractive environment is still below average or low, i.e. e. 65.7% of students who have not yet graduated from school at the designated KKM. The average score must be at least 70. In cycle II, the grade point average is 66 points higher than it was in cycle I, which was only 65 points, and the student pass rate is 65 points higher than it was in cycle I, which was not 65 points. This information can be gleaned from each student's grade results, which are completed in accordance with the established KKM, which is 70.
Mengidentifikasi Kesulitan Belajar Siswa pada Pelajaran Biologi secara Daring di SMAN 13 Takalar

ABSTRACTThis study aims to determine the profile of student learning difficulties and factors that affect student learning difficulties in online biology lessons at SMAN 13 Takalar. The research method uses a quantitative descriptive approach. The data collection technique is in the form of a questionnaire of student responses related to learning difficulties in biology lessons in online learning at SMAN 13 Takalar. The results of the descriptive analysis of the study showed that the profile of student learning difficulties in online biology lessons at SMAN 13 Takalar, namely mastery of student material is classified as a low category, student learning time is classified as a low category, student learning achievement achievement is classified as a medium category, student focus is classified as a medium category, student enthusiasm is classified as a medium category, student learning behavior is classified as a medium category and the use of online learning applications is classified as a medium category. Keywords: Learning difficulties, online learning, SMAN 13 Takalar.
HASIL ANALISIS STATISTIK, DIFERENSIAL, DAN KUALITAS CITRA TERHADAP KOMPOSISI LOGISTIC CIRCLE MAP

The problem of data security is still a matter of concern in the digital era like today. An effective security method is needed and it is difficult to break into. Encryption is an effective method of ensuring data security. The encryption algorithm is built using a chaotic cipher base so that performance in securing data and information increases. The new chaotic composition resulting from two chaotic maps will have better randomness. Logistic Map and Circle Map were chosen because they have good randomness. The new chaotic composition is called the Logistic Circle Map (LC Map). By bifurcation diagram test, Lyapunov Exponent, and NIST random test, the best density occurs at X_0=0.9 , K = 1000, r = 3.7 and Ω = 0.4. The new chaotic composition parameters have passed all 16 NIST (National Institute of Standards and Technology) tests. There are four parameters used in this study in order to obtain a sufficiently high key space, namely 1.296 ×〖 10〗^84 combinations and having a high level of lock sensitivity of 10−17. The image encryption algorithm of the LC Map function has high resistance against various attacks. This can be proven by carrying out key space tests, sensitivity tests for key spaces, histogram tests, entropy, correlation coefficients, and differential tests using the UACI NPCR. The test results on digital image decryption using the Mean Square Error (MSE) show a result of 0 and the Peak Signal to Noise Ratio (PSNR) shows a result of ∞. That is, the encrypted image is similar to the original image.
Conditional Generative Adversarial Networks for Data Augmentation of a Neonatal Image Dataset

In today’s neonatal intensive care units, monitoring vital signs such as heart rate and respiration is fundamental for neonatal care. However, the attached sensors and electrodes restrict movement and can cause medical-adhesive-related skin injuries due to the immature skin of preterm infants, which may lead to serious complications. Thus, unobtrusive camera-based monitoring techniques in combination with image processing algorithms based on deep learning have the potential to allow cable-free vital signs measurements. Since the accuracy of deep-learning-based methods depends on the amount of training data, proper validation of the algorithms is difficult due to the limited image data of neonates. In order to enlarge such datasets, this study investigates the application of a conditional generative adversarial network for data augmentation by using edge detection frames from neonates to create RGB images. Different edge detection algorithms were used to validate the input images’ effect on the adversarial network’s generator. The state-of-the-art network architecture Pix2PixHD was adapted, and several hyperparameters were optimized. The quality of the generated RGB images was evaluated using a Mechanical Turk-like multistage survey conducted by 30 volunteers and the FID score. In a fake-only stage, 23% of the images were categorized as real. A direct comparison of generated and real (manually augmented) images revealed that 28% of the fake data were evaluated as more realistic. An FID score of 103.82 was achieved. Therefore, the conducted study shows promising results for the training and application of conditional generative adversarial networks to augment highly limited neonatal image datasets.
<b>Skin</b> in The Game

The sense of touch has been a significant factor missing from robotics up to now. However, an Edinburgh-based company has developed a product that could change all that. Andrew Wade reports.
Edge Data Centres Need Embedded Server Technologies

Reduced latency and energy consumption in long-distance data traffic will require computing power at the edge, meaning that embedded and server technologies need to converge.
Time-Space Tradeoffs for Element Distinctness and Set Intersection via Pseudorandomness

In the ELEMENT DISTINCTNESS problem, one is given an array a1,…, an of integers from [poly(n)] and is tasked to decide if {ai} are mutually distinct. Beame, Clifford and Machmouchi (FOCS 2013) gave a low-space algorithm for this problem that runs in space S(n) and time T(n) where T(n) ≤ Õ(n3/2/S(n)1/2), assuming a random oracle (i.e., random access to polynomially many random bits). A recent breakthrough by Chen, Jin, Williams and Wu (SODA 2022) showed how to remove the random oracle assumption in the regime S(n) = polylog(n) and T(n) = Õ(n3/2). They designed the first truly polylog(n)-space, Õ(n3/2)-time algorithm by constructing a small family of hash functions H ⊆ {h|h : [poly(n)] → [n]} with a certain pseudorandom property.In this paper, we give a significantly simplified analysis of the pseudorandom hash family by Chen et al. Our analysis clearly identifies the key pseudorandom property required to fool the BCM algorithm, allowing us to explore the full potential of this construction. Based on our new analysis, we show the following.• As our main result, we give a time-space tradeoff for ELEMENT DISTINCTNESS without random oracle. Namely, for every S(n),T(n) such that T ≈ Õ(n3/2/S(n)1/2), our algorithm can solve the problem in space S(n) and time T(n). Our algorithm also works for a related problem SET INTERSECTION, for which this tradeoff is tight due to a matching lower bound by Dinur (Eurocrypt 2020).• As a direct application of our technique, we show a more general pseudorandom property of the hash family, which we call the “c-connecting” property. It might be of independent interest.• The construction by Chen et al. needs O(log3 n log log n) random bits to sample the pseudorandom hash function. We slightly improve the seed length to O (log3 n).* The full version of the paper can be accessed at https://arxiv.org/abs/2210.07534
Private Query Release via the Johnson-Lindenstrauss Transform

We introduce a new method for releasing answers to statistical queries with differential privacy, based on the Johnson-Lindenstrauss lemma. The key idea is to randomly project the query answers to a lower dimensional space so that the distance between any two vectors of feasible query answers is preserved up to an additive error. Then we answer the projected queries using a simple noise-adding mechanism, and lift the answers up to the original dimension. Using this method, we give, for the first time, purely differentially private mechanisms with optimal worst case sample complexity under average error for answering a workload of k queries over a universe of size N. As other applications, we give the first purely private efficient mechanisms with optimal sample complexity for computing the covariance of a bounded high-dimensional distribution, and for answering 2-way marginal queries. We also show that, up to the dependence on the error, a variant of our mechanism is nearly optimal for every given query workload.
Superpolynomial lower bounds for decision tree learning and testing

We establish new hardness results for decision tree optimization problems, adding to a line of work that dates back to Hyafil and Rivest in 1976. We prove, under the randomized exponential time hypothesis, superpolynomial runtime lower bounds for two basic problems: given an explicit representation of a function f and a generator for a distribution D○ construct a small decision tree approximator for f under D, and○ decide if there is a small decision tree approximator for f under D.Our results imply new lower bounds for distribution-free PAC learning and testing of decision trees, settings in which the algorithm only has restricted access to f and D. Specifically, we get that:○ n-variable size-s decision trees cannot be properly PAC learned in time nÕ(log log s), and○ depth-d decision trees cannot be tested in time exp(dO(1)).For learning, the previous best lower bound only ruled out poly(n)-time algorithms (Alekhnovich, Braverman, Feldman, Klivans, and Pitassi, 2009). For testing, recent work gives similar though incomparable lower bounds in the setting where f is random and D is nonexplicit (Blais, Ferreira Pinto Jr., and Harms, 2021).Assuming a plausible conjecture on the hardness of SET-COVER, we show that our lower bound for properly PAC learning decision trees can be improved to nΩ(log s), matching the best known upper bound of nO(log s) due to Ehrenfeucht and Haussler (1989).We obtain our results within a unified framework that leverages recent progress in two different lines of work: the inapproximability of SET-COVER and XOR lemmas for query complexity. Our framework is versatile and yields results for related concept classes such as juntas and DNF formulas.∗ The full version of the paper can be accessed at https://arxiv.org/abs/2210.06375
Almost Tight Error Bounds on Differentially Private Continual Counting

The first large-scale deployment of private federated learning uses differentially private counting in the continual release model as a subroutine (Google AI blog titled “Federated Learning with Formal Differential Privacy Guarantees” on February 28, 2022). For this and several other applications, it is crucial to use a continual counting mechanism with small mean squared error. In this case, a concrete (or non-asymptotic) bound on the error is very relevant to reduce the privacy parameter ε as much as possible, and hence, it is important to improve upon the constant factor in the error term. The standard mechanism for continual counting, and the one used in the above deployment, is the binary mechanism. We present a novel mechanism and show that its mean squared error is both asymptotically optimal and a factor 10 smaller than the error of the binary mechanism. We also show that the constants in our analysis are almost tight by giving non-asymptotic lower and upper bounds that differ only in the constants of lower-order terms. Our mechanism also has the advantage of taking only constant time per release, while the binary mechanism takes O(log n) time, where n is the total number of released data values. Our algorithm is a matrix mechanism for the counting matrix. We also use our explicit factorization of the counting matrix to give an upper bound on the excess risk of the matrix mechanism-based private learning algorithm of Denisov, McMahan, Rush, Smith, and Thakurta (NeurIPS 2022).Our lower bound for any continual counting mechanism is the first tight lower bound on continual counting under (ε, δ) -differential privacy and it holds against a non-adaptive adversary. It is achieved using a new lower bound on a certain factorization norm, denoted by γ f (·), in terms of the singular values of the matrix. In particular, we show that for any complex matrix, A ∊ℂm × n,where ||·|| denotes the Schatten-1 norm. We believe this technique will be useful in proving lower bounds for a larger class of linear queries. To illustrate the power of this technique, we show the first lower bound on the mean squared error for answering parity queries. This bound applies to the non-continual setting and is asymptotically tight.
Online Lewis Weight Sampling

The seminal work of Cohen and Peng [CP15] (STOC 2015) introduced Lewis weight sampling to the theoretical computer science community, which yields fast row sampling algorithms for approximating d-dimensional subspaces of ℓp up to (1 + ε) relative error. Several works have extended this important primitive to other settings, including the online coreset and sliding window models [BDM+20] (FOCS 2020) as well as the adversarial streaming model [BHM+21] (NeurIPS 2021). However, these results are only for p ∈ {1, 2}, and results for p = 1 require a suboptimal Õ(d2/ε2) samples.In this work, we design the first nearly optimal ℓp subspace embeddings for all p ∈ (0, ∞) in the online coreset, sliding window, and the adversarial streaming models. In all three models, our algorithms store Õ(d/ε2) rows for p ∈ (0, 2) and Õ(dp/2/ε2) rows for p ∈ (2, ∞). This answers a substantial generalization of the main open question of [BDM+20], and gives the first results for all p ∉ {1, 2} and achieves nearly optimal sample complexities for all p.Towards our result, we give the first analysis of “one-shot” Lewis weight sampling of sampling rows proportionally to their Lewis weights, which gives a sample complexity of Õ(dp/2/ε2) rows for p > 2. Previously, such a sampling scheme was only known to have a sample complexity of Õ(dp/2/ε5) [CP15], whereas a bound of Õ(dp/2/ε2) is known if a more sophisticated recursive sampling algorithm is used [MMWY21, LT91]. Note that the recursive sampling strategy cannot be implemented in an online setting, thus necessitating an analysis of one-shot Lewis weight sampling. Perhaps surprisingly, our analysis crucially uses a novel connection to online numerical linear algebra, even for offline Lewis weight sampling.As an application, we obtain the first one-pass streaming coreset algorithms for (1 + ε) approximation of important generalized linear models, such as logistic regression and p-probit regression. Our upper bounds are parameterized by a complexity parameter μ introduced by [MSSW18], and we also provide the first lower bounds showing that a linear dependence on μ is necessary.* The full version of the paper can be accessed at https://arxiv.org/abs/2207.08268
Tight Bounds for Monotone Minimal Perfect Hashing

The monotone minimal perfect hash function (MMPHF) problem is the following indexing problem. Given a set S = {sı,…, sn} of n distinct keys from a universe U of size u, create a data structure D that answers the following query:Solutions to the MMPHF problem are in widespread use in both theory and practice.The best upper bound known for the problem encodes D in O(n log log log u) bits and performs queries in O (log u) time. It has been an open problem to either improve the space upper bound or to show that this somewhat odd looking bound is tight.In this paper, we show the latter: any data structure (deterministic or randomized) for monotone minimal perfect hashing of any collection of n elements from a universe of size u requires Ω(n · log log log u) expected bits to answer every query correctly.We achieve our lower bound by defining a graph G where the nodes are the possible inputs and where two nodes are adjacent if they cannot share the same D. The size of D is then lower bounded by the log of the chromatic number of G. Finally, we show that the fractional chromatic number (and hence the chromatic number) of G is lower bounded by 2Ω(nlog log log u).* A full version of the paper is available at: https://arxiv.org/abs/2207.10556.
Low Degree Testing over the Reals

We study the problem of testing whether a function f : ℝn → ℝ is a polynomial of degree at most d in the distribution-free testing model. Here, the distance between functions is measured with respect to an unknown distribution D over ℝn from which we can draw samples. In contrast to previous work, we do not assume that D has finite support.We design a tester that given query access to f, and sample access to D, makes poly(d/ε) many queries to f, accepts with probability 1 if f is a polynomial of degree d, and rejects with probability at least 2/3 if every degree-d polynomial P disagrees with f on a set of mass at least ε with respect to D. Our result also holds under mild assumptions when we receive only a polynomial number of bits of precision for each query to f, or when f can only be queried on rational points representable using a logarithmic number of bits. Along the way, we prove a new stability theorem for multivariate polynomials that may be of independent interest.* The arXiv version of the paper can be accessed at https://arxiv.org/abs/2204.08404
A comprehensive review of security threats and solutions for the online social networks industry

The term “cyber threats” refers to the new category of hazards that have emerged with the rapid development and widespread use of computing technologies, as well as our growing reliance on them. This article presents an in-depth study of a variety of security and privacy threats directed at different types of users of social media sites. Furthermore, it focuses on different risks while sharing multimedia content across social networking platforms, and discusses relevant prevention measures and techniques. It also shares methods, tools, and mechanisms for safer usage of online social media platforms, which have been categorized based on their providers including commercial, open source, and academic solutions.
Examples of Synonym Analysis Using Linguistic Data: In Example of TOKUBETSU, TOKUSHU

In this paper, using TOKUBETSU and TOKUSHU as examples, I examined how to analyze the differences in synonyms through close observation of the actual state of usage shown in the corpus, which is large-scale linguistic data. The criteria for distinguishing between TOKUBETSU and TOKUSHU are presented in detail from three perspectives: frequency, context, and genre. First, in terms of frequency ratio, 73% of TOKUBETSU>24% of TOKUSHU(BCCWJ), 70.5% of TOKUBETSU>26.8% of TOKUSHU (NLT), indicating the same trend regardless of data. From this, the superiority of TOKUBETSU in both general usage and the order of introduction in vocabulary education was confirmed. Second, the discriminative criterion was extracted through the semantic tendency of the trailing noun in the context of use. TOKUBETSU indicates that “the speaker's subjective judgment should distinguish it from the ordinary” or “the objective content or intensity itself is not ordinary and therefore distinguished,” while TOKUSHU indicates “something that is different from the average in quality.” Third, in terms of the genre of use, TOKUBETSU is more often used in public documents and scenes such as laws, parliamentary minutes, publicity papers, textbooks, and white papers than TOKUSHU, and is also used more frequently in everyday scenes such as web blogs.