Reviewerly/exHarmony huggingface dataset:
Watermarking Using Energy-LSB Embedded Method

Digital watermarking technology is increasingly used to protect copyright and demonstrate ownership of digital multimedia (such as text, music, photos, and videos). In order to safeguard intellectual property rights and rules of ownership for multimedia, this project suggests a text watermark algorithm. The process of hiding little text or grayscale images is the main focus, though. The masking of a watermark text in a high-color or high-density area of the block picture has been proposed using an embedding technique based on an energy function and the Least Significant Bits (LSB) method. Even with various format types and picture sizes chosen to conceal and cover a changing message size, the quality results demonstrate that the watermark image suffers from less distortion than the cover image, and the suggested algorithm is powerful to conceal a random watermark text even with smaller block sizes. An additional optional choice to encrypt the text watermark before embedding is also recommended because doing so would make it harder for hackers to read the text. This text can be encrypted using the Caesar cipher method before embedding is implemented in images.The experimental results of using the suggested algorithm for embedding and extracting watermark text for various sizes in a large number of images were satisfactory, giving a level of peak signal-to-noise ratio (PSNR) and Signal to Noise Ratio (SNR) with low mean square error (MSE) values. However, PSNR degrades more quickly than LBS as the watermark text size increases, so it was determined that it is more suitable for applying a watermark rather than a stego It is employed in order to share information securely
A Novel Arabic Words Recognition System Using Hyperplane Classifier

Topic of exhaustive study for about past decades has been carried out in machine imitation of human reading. a small number of investigates have been accepted on the detection of cursive font writing like Arabic texts for its individual challenge and difficulty .In this work, a novel technique for automatic Arabic font recognition is proposed to demonstrate an suitable recognition rate for multi fonts styles and multi sizes of Arabic word images.&#x0D; The scheme can be classified into a number of steps. First, segmenting Arabic line into words depending on the vertical projection and dynamic threshold then we implicated each Arabic word as a class by ignoring segmenting the word into characters .Second ,normalizing step, the size of Arabic word images varies from each other .The system converts the images that contribution into a new size that is divisible by "N" without remainder, to decrease the difficulty of feature extraction and recognition of the system that may allow images from different resources, Third, feature extraction step which is based on apply the ratio of vertical sliding strips as a features. Finally, multi class support vector machine (one versus one technique)is used as a classifier .This method was estimated on off line printed fonts, five Arabic fonts, (Andalus, Arial, Simplified Arabic, Tahoma and Traditional Arabic) were used and the average recognition rate of all fonts was 95.744%.
Hambatan Pembelajaran IPS pada Sekolah Satu Atap

The background of this research is the emergence of several obstacles in the implementation of social studies learning at SMP Negeri 23 Batam which is a one-roof school. So far, learning problems have always been assessed in terms of the background of students who are island children where they are identified with children who are lazy and have a low learning ethic. Researchers believe that the problem is not only from the side of the students but also from the side of the teaching staff and all elements that support the learning process. The purpose of this study was to explain the barriers to social studies learning in one-roof schools from the point of view of the teaching staff and all parties involved. The research location was carried out at SMP Negeri 23 Batam which is a one-roof school. The research method used is descriptive qualitative. Instruments and data collection techniques are observation, interviews. Data analysis techniques are data reduction, data presentation, drawing conclusions. The conclusion of this research is that there are several problems that become obstacles to Social Studies learning in One-Roof schools, namely the not yet optimal use of eco media or simple media as learning by teachers in schools, the not optimal use of smartphones to introduce digital media for learning in schools, the need to improve the ethos the work of teaching staff in island schools, the teaching and non-educational staff in schools are not optimal yet towards excellent service, the implementation of rules and regulations in schools is not yet optimal
Constrained Portfolio Management Using Action Space Decomposition for Reinforcement Learning

Abstract Financial portfolio managers typically face multi-period optimization tasks such as short-selling or investing at least a particular portion of the portfolio in a specific industry sector. A common approach to tackle these problems is to use constrained Markov decision process (CMDP) methods, which may suffer from sample inefficiency, hyperparameter tuning, and lack of guarantees for constraint violations. In this paper, we propose Action Space Decomposition Based Optimization (ADBO) for optimizing a more straightforward surrogate task that allows actions to be mapped back to the original task. We examine our method on two real-world data portfolio construction tasks. The results show that our new approach consistently outperforms state-of-the-art benchmark approaches for general CMDPs.
Regularization of the Policy Updates for Stabilizing Mean Field Games

This work studies non-cooperative Multi-Agent Reinforcement Learning (MARL) where multiple agents interact in the same environment and whose goal is to maximize the individual returns. Challenges arise when scaling up the number of agents due to the resultant non-stationarity that the many agents introduce. In order to address this issue, Mean Field Games (MFG) rely on the symmetry and homogeneity assumptions to approximate games with very large populations. Recently, deep Reinforcement Learning has been used to scale MFG to games with larger number of states. Current methods rely on smoothing techniques such as averaging the q-values or the updates on the mean-field distribution. This work presents a different approach to stabilize the learning based on proximal updates on the mean-field policy. We name our algorithm Mean Field Proximal Policy Optimization (MF-PPO), and we empirically show the effectiveness of our method in the OpenSpiel framework.
pFedV: Mitigating Feature Distribution Skewness via Personalized Federated Learning with Variational Distribution Constraints

Statistical heterogeneity, especially feature distribution skewness, among the distributed data is a common phenomenon in practice, which is a challenging problem in federated learning that can lead to a degradation in the performance of the aggregated global model. In this paper, we introduce pFedV, a novel approach that leverages a variational inference perspective by incorporating a variational distribution into neural networks. During training, we add the KL-divergence term to the loss function to constrain the output distribution of layers for feature extraction and personalize the final layer of models. The experimental results demonstrate the effectiveness of our approaches in mitigating the distribution shift in feature space in federated learning.
Reduction from Complementary-Label Learning to Probability Estimates

Complementary-Label Learning (CLL) is a weakly-supervised learning problem that aims to learn a multi-class classifier from only complementary labels, which indicate a class to which an instance does not belong. Existing approaches mainly adopt the paradigm of reduction to ordinary classification, which applies specific transformations and surrogate losses to connect CLL back to ordinary classification. Those approaches, however, face several limitations, such as the tendency to overfit. In this paper, we sidestep those limitations with a novel perspective–reduction to probability estimates of complementary classes. We prove that accurate probability estimates of complementary labels lead to good classifiers through a simple decoding step. The proof establishes a reduction framework from CLL to probability estimates. The framework offers explanations of several key CLL approaches as its special cases and allows us to design an improved algorithm that is more robust in noisy environments. The framework also suggests a validation procedure based on the quality of probability estimates, offering a way to validate models with only CLs. The flexible framework opens a wide range of unexplored opportunities in using deep and non-deep models for probability estimates to solve CLL. Empirical experiments further verified the framework’s efficacy and robustness in various settings. The full paper can be accessed at https://arxiv.org/abs/2209.09500 .
Achieving Provable Byzantine Fault-tolerance in a Semi-honest Federated Learning Setting

Federated learning (FL) is a suite of technology that allows multiple distributed participants to collaboratively build a global machine learning model without disclosing private datasets to each other. We consider an FL setting in which there may exist both a) semi-honest participants who aim to eavesdrop on other participants’ private datasets; and b) Byzantine participants who aim to degrade the performances of the global model by submitting detrimental model updates. The proposed framework leverages the Expectation-Maximization algorithm first in E-step to estimate unknown participant membership, respectively, of Byzantine and benign participants, and in M-step to optimize the global model performance by excluding malicious model updates uploaded by Byzantine participants. One novel feature of the proposed method, which facilitates reliable detection of Byzantine participants even with HE or MPC protections, is to estimate participant membership based on the performances of a set of randomly generated candidate models evaluated by all participants. The extensive experiments and theoretical analysis demonstrate that our framework guarantees Byzantine Fault-tolerance in various federated learning settings with private-preserving mechanisms.
Temporal characterization of Alzheimer's Disease with sequences of clinical records

Alzheimer's Disease (AD) is a complex clinical phenotype with unprecedented social and economic tolls on an ageing global population. Real-world data (RWD) from electronic health records (EHRs) offer opportunities to accelerate precision drug development and scale epidemiological research on AD. A precise characterization of AD cohorts is needed to address the noise abundant in RWD.We conducted a retrospective cohort study to develop and test computational models for AD cohort identification using clinical data from 8 Massachusetts healthcare systems. We mined temporal representations from EHR data using the transitive sequential pattern mining algorithm (tSPM) to train and validate our models. We then tested our models against a held-out test set from a review of medical records to adjudicate the presence of AD. We trained two classes of Machine Learning models, using Gradient Boosting Machine (GBM), to compare the utility of AD diagnosis records versus the tSPM temporal representations (comprising sequences of diagnosis and medication observations) from electronic medical records for characterizing AD cohorts.In a group of 4985 patients, we identified 219 tSPM temporal representations (i.e., transitive sequences) of medical records for constructing the best classification models. The models with sequential features improved AD classification by a magnitude of 3-16 percent over the use of AD diagnosis codes alone. The computed cohort included 663 patients, 35 of whom had no record of AD. Six groups of tSPM sequences were identified for characterizing the AD cohorts.We present sequential patterns of diagnosis and medication codes from electronic medical records, as digital markers of Alzheimer's Disease. Classification algorithms developed on sequential patterns can replace standard features from EHRs to enrich phenotype modelling.National Institutes of Health: the National Institute on Aging (RF1AG074372) and the National Institute of Allergy and Infectious Diseases (R01AI165535).
Naturalness is gradient in morphological paradigms: Evidence from positional splits

Agreement markers that refer to the same feature or argument tend to be found in the same position (e.g., all subject agreement markers as suffixes, all object agreement markers as prefixes). However, little is known about the exceptions to this trend: cases where different values of the same feature are marked in different positions in the word (i.e., positional splits). In this study, we explore the positional properties of subject and object person-number agreement markers in a phylogenetically diverse sample of 227 languages. We find that the recurrence of a positional split is proportional to its degree of naturalness, that is, to the amount of shared feature values amongst the forms with the same positional arrangement. Natural patterns (e.g., where prefixal forms all share SG and suffixal forms all share PL) are over-represented in natural languages compared to a random baseline. The most unnatural patterns are underrepresented, and splits with an intermediate level of unnaturalness occur at around chance levels. We hypothesise that this graded bias for naturalness is grounded in a preference for morphological similarity amongst semantically similar forms during language learning. To test this hypothesis we conducted two online artificial language learning experiments where we trained and tested participants on person-number verbal agreement paradigms of different sizes with positional splits of different degrees of naturalness. We found that their relative learnability is also gradient, again proportional to the amount of feature value overlap, thus matching the observed cross-linguistic tendencies. Our findings support the notion that semantic similarity shapes the evolution of morphological structure in person-number verbal agreement systems and that it does so in a gradient way.
A novel framework for crowd counting using video and audio

Crowd counting is significant in many areas. The existing methods have poor accuracy for perspective scenes and low illumination scenes. Besides, the existing audio-assisted methods only use local audio, which fails to provide the spatial feature information of sound in all directions in space. To alleviate the above problems, a novel framework named Video and Audio-assisted Crowd Counting Network (VACCNet) is proposed. The framework consists of two submodules: Video Crowd Counting (VCC) module and Audio-assisted Crowd Counting (ACC) module. The visual features from the VCC module and the fused audio features from the ACC module are further combined to get the final density map. To prove the effects of VACCNet, a new self-collected dataset named multiPle dIrection Assistance couNting netwOrk (PIANO) is built. The experimental results based on existing benchmarks and PIANO show that the proposed method has a 14.23% improvement averagely to the conventional methods.
TinyLedger: A Lightweight Blockchain Ledger Protocol for the MEC Network

With the development of the Internet of Things, multi-access edge computing (MEC) is widely used in industrial IoT, intelligent transport systems, and IoT data management. As a decentralized, tamper-proof, multiparty trusted ledger technology, blockchain can enhance MEC security, privacy, and automatic use of resources. However, highly redundant storage and complex consensus prevent the blockchain from being directly deployed to edge computing nodes with limited resources. Unlike existing lightweight schemes, which do not consider liberating transaction data from blocks, this paper proposes a lightweight blockchain ledger protocol for MEC networks. The novelty of this work is that the transaction data in the block is compressed into a succinct proof through the zero-knowledge proof algorithm. To reduce the storage of block data, this paper proposes the proof-of-state-transfer model in which nodes verify the correctness of the global state transfer in the block without transactions. To reduce the communication complexity of consensus, this paper proposes a lightweight consensus protocol, which reduces the communication complexity of block voting and view switching to O(n). This paper develops the prototype system of TinyLedger to experiment and evaluate the performance of the system in terms of storage, verification, throughput, and communication. Experimental results show that compared to Ethereum, TinyLedger reduces storage capacity by 98.16%, communication volume by 74.53%, and block verification time by 87%.
Bespoke pulse design for robust rapid two-qubit gates with trapped ions

Two-qubit gate performance is vital for scaling up ion-trap quantum computing. Optimized quantum control is needed to achieve reductions in gate duration and gate error rate. We describe two-qubit gates with addressed Raman beams within a linear trapped-ion chain by a quantum master equation (QME). The QME incorporates the single-ion two-photon effective Rabi frequency, Autler-Townes and vibrational Bloch-Siegert energy shifts, off-resonant transitions, Raman and Rayleigh scattering, laser-power fluctuations, motional heating, cross-Kerr phonon coupling, laser spillover, asymmetric addressing beams, and an imperfect initial motional ground state, with no fitting parameters, whereas state-of-the-art methods are oblivious to these effects in the gate design procedure. We employ global optimization to design pulse sequences for achieving a robust rapid two-qubit gate for a simulated chain of seven trapped $^{171}\mathrm{Yb}^{+}$ ions by optimizing over numerically integrated QME solutions. Here, robust means resilient against slow drift of motional frequencies, and rapid means gate execution where the effective Rabi frequency is comparable to the detuning of the laser from the ion's bare electronic transition. Our robust quantum control delivers rapid high-quality two-qubit gates in long ion chains, enabling scalable quantum computing with trapped ions.
Competing Patterns in Bernoulli Sequence of Trials

Consider performing a sequence of Bernoulli trials (each resulting in either a success, denoted S, or a failure F, with a probability of p and : 1spectively) until one of m specific strings (or patterns) of consecutive outcomes is generated.This can be seen as a game where m players select one such pattern each and the one whose pattern occurs first wins.We present symbolic formulas for the m probabilities of winning, and for the mean number of trials and the corresponding standard deviation to complete this game.Several numerical examples are presented, including a search for optimal strategy.
Linear vs. non-linear translation in parallel text reading

This study investigated the effect of differences between linear and non-linear translations on reading behavior in a situation where both the source and target texts were presented for revision. Linear translations, which are relatively close to the source text in word order (with a low cross value), and non-linear translations, which are relatively remote (with a high cross value), were created. An experiment was conducted in which participants compared these two types of translations with the source text to detect translation errors. Using the collected data, models were constructed to predict the reading time of the target text and the number of regressions to the source or target text from the difference between the linear and non-linear translations. When reading both the source and target texts, the difference between the linear and non-linear translations significantly affected the reading time of the target text. In other words, when the word order of the target text differed significantly from that of the source text, readers’ cognitive effort was greater. The difference between the linear and non-linear translations was also significantly associated with the number of regressions, such that if the difference in word order between the target text and source text is large, readers needed significant regression.
Forecasting changes of the flow regime at deep geothermal wells based on high resolution sensor data and low resolution chemical analyses

Abstract. Geothermal waters provide a great resource to generate clean energy, however, there is a notorious lack of high quality data on these waters. The scarcity of deep geothermal aquifer information is largely due to inaccessibility and high analysis costs. However, multiple operators use geothermal wells in Lower Bavaria and Upper Austria for balneological (medical and wellness) applications as well as for heat mining purposes. The state of the art sampling strategy budgets for a sampling frequency of 1 year. Previous studies have shown that robust groundwater data requires sampling intervals of 1–3 months, however, these studies are based on shallow aquifers which are more likely to be influenced by seasonal changes in meteorological conditions. This study set out to assess whether yearly sampling adequately represents sub-yearly hydrochemical fluctuations in the aquifer by comparing yearly with quasi-continuous hydrochemical data at two wells in southeast Germany by assessing mean, trend and seasonality detection among the high and low temporal resolution data sets. Furthermore, the ability to produce reliable forecasts based on yearly data was examined. In order to test the applicability of virtual sensors to elevate the information content of yearly data, correlations between the individual parameters were assessed. The results of this study show that seasonal hydrochemical variations take place in deep aquifers, and are not adequately represented by yearly data points, as they are typically gathered at similar production states of the well and do not show varying states throughout the year. Forecasting on the basis of yearly data does not represent the data range of currently measured continuous data. The limited data availability did not allow for strong correlations to be determined. We found that annual measurements, if taken at regular intervals and roughly the same production rates, represent only a snapshot of the possible hydrochemical compositions. Neither mean values, trends nor seasonality was accurately captured by yearly data. This could lead to a violation of stability criteria for mineral water, or to problems in the geothermal operation (scalings, degassing). We thus recommend a new testing regime of at least 3 samples a year. While not a replacement for the detailed analyses, under the right circumstances, and when trained with more substantial data sets, viertual sensors provide a robust method in this setting to trigger further actions.