Reviewerly/exHarmony huggingface dataset:
Non-malleable extractors and non-malleable codes: partially optimal constructions

The recent line of study on randomness extractors has been a great success, resulting in exciting new techniques, new connections, and breakthroughs to long standing open problems in several seemingly different topics. These include seeded non-malleable extractors, privacy amplification protocols with an active adversary, independent source extractors (and explicit Ramsey graphs), and non-malleable codes in the split state model. Previously, the best constructions are given in [54]: seeded non-malleable extractors with seed length and entropy requirement O(log n + log(1/∈) log log(1/∈)) for error ∈; two-round privacy amplification protocols with optimal entropy loss for security parameter up to Ω(k/ log k), where k is the entropy of the shared weak source; two-source extractors for entropy O(log n log log n); and non-malleable codes in the 2-split state model with rate Ω(1/ log n). However, in all cases there is still a gap to optimum and the motivation to close this gap remains strong.In this paper, we introduce a set of new techniques to further push the frontier in the above questions. Our techniques lead to improvements in all of the above questions, and in several cases partially optimal constructions. This is in contrast to all previous work, which only obtain close to optimal constructions. Specifically, we obtain:1. A seeded non-malleable extractor with seed length O(log n) + log1+o(1) (1/∈) and entropy requirement O(log log n + log(1/∈)), where the entropy requirement is asymptotically optimal by a recent result of Gur and Shinkar [40];2. A two-round privacy amplification protocol with optimal entropy loss for security parameter up to Ω(k), which solves the privacy amplification problem completely;13. A two-source extractor for entropy [EQUATION], which also gives an explicit Ramsey graph on N vertices with no clique or independent set of size (log N) [EQUATION]; and4. The first explicit non-malleable code in the 2-split state model with constant rate, which has been a major goal in the study of non-malleable codes for quite some time. One small caveat is that the error of this code is only (an arbitrarily small) constant, but we can also achieve negligible error with rate Ω(log log log n/ log log n), which already improves the rate in [54] exponentially.We believe our new techniques can help to eventually obtain completely optimal constructions in the above questions, and may have applications in other settings.
Analysis and Detection of the Degrees and Direction of Correlations between Key Indicators of Physical Fitness of 10-12-year-old Hockey Players

Aim of the study was to determine physical activity's part of daily energy expenditure by heart rate monitoring and using data to control athletes' nutritional intake.Group of 10 male and 4 female wrestlers (age = 21 ± 1.8) at national level, who train 15 hours per week served as subjects in this investigation.The 72-hour HR recording was performed with a TEMEO cardiotelemetric system (made in Bulgaria).The energy expenditure during physical activity is determined by Method 1 of Hiilloskorpi et al. (2003).The determined daily energy expenditure is compared to the theoretically calculated.The deference is less than 100 kcal, but if subjects change the intensity, volume or duration of the workouts or increase their number, the difference will be much more evident.
An Intelligent System to Diagnose Chikungunya under Uncertainty

Chikungunya is a virus-related disease, bring about by the virus called CHIKV that spreads throughmosquito biting. This virus first found in Tanzania, while blood from patients was isolated. Thecom ...
Predicting Sensor Events, Activities, and Time of Occurrence Using Binary Sensor Data From Homes With Older Adults

We present a comprehensive study of state-of-the-art algorithms for the prediction of sensor events and activities of daily living in smart homes.Data have been collected from eight smart homes with real users and 13-17 binary sensors each -including motion, magnetic, and power sensors.We apply two probabilistic methods, namely Sequence Prediction via Enhanced Episode Discovery and Active LeZi, as well as Long Short-Term Memory Recurrent Neural Network, in order to predict the next sensor event in a sequence.We compare these with respect to the required number of preceding sensor events to predict the next, the necessary amount of data to achieve good accuracy and convergence, as well as varying the number of sensors in the dataset.The best-performing method is further improved by including information on the time of occurrence to predict the next sensor event only, and in addition to predict both the next sensor event and the mean time of occurrence in the same model.Subsequently, we apply transfer learning across apartments to investigate its applicability, advantages, and limitations for this setup.Our best implementation achieved an accuracy of 77-87% for predicting the next sensor event, and an accuracy of 73-83% when predicting both the next sensor event and the mean time elapsed to the next sensor event.Finally, we investigate the performance of predicting daily living activities derived from the sensor events.We can predict activities with an accuracy of 61-90%, depending on the apartment.
Deep Embedded Clustering With Adversarial Distribution Adaptation

Clustering methods based on deep neural networks have been extensively studied and applied in data mining. Most existing unsupervised deep embedded clustering methods jointly learn feature representations and cluster assignments with deep neural networks. However, to the best of our knowledge, most of them ignore the relevance between representation learning and clustering, so that fail to couple feature representation learning and cluster assignments effectively, which leads to non-representative features for clustering and this in turn hurts clustering performance. In this paper, we propose Adversarial Deep Embedded Clustering (ADEC), a novel unsupervised clustering method based on adversarial auto-encoder (AAE) and k -means clustering method. Specifically, ADEC matches distribution of feature representations with the given prior distribution to preserve the data structure with AAE, and k -means based on distribution distance metrics is conducted for clustering with these feature representations, simultaneously. ADEC optimizes a clustering objective iteratively with backpropagation algorithm in learning AAE from data space to feature space. Extensive experiments have demonstrated the effectiveness of our proposed method on images clustering compared to several strong baseline methods.
MEMS gyroscope wavelet de-noising method based on redundancy and sparse representation

Since the wavelet analysis does not need exact noise model, wavelet decomposition is suitable for analyzing the signal with complex noise characteristics. In this paper, a real-time wavelet de-noising method is used for the error compensation of MEMS gyroscope. In order to avoid the shortcomings that wavelet threshold processing ignores the wavelet coefficient integrity, the sparse and redundant representation methods are used to optimize the wavelet coefficients. The reverse use of compressed sensing algorithm represents the low-frequency wavelet coefficients redundantly, and optimizes the sparse coefficients, and then reconstructs the low-frequency wavelet coefficients. The sparse representation method is used to sparsely represent the high-frequency wavelet coefficients, and the high-frequency wavelet coefficients are optimized. Then the optimized high and low frequency wavelet coefficients are used to reconstruct the original signal and the lag correction algorithm is used to reduce the boundary effect of wavelet decomposition. Finally, the feasibility of the algorithm is verified by simulation and experiments, which provides a new idea for wavelet de-noising.
Child-Generated Personas to Aid Design Across Cultures

Designers frequently use personas to model potential users, but these personas need to be accurate portrayals of people. With personas needed to facilitate a cross-cultural participatory design project, it was recognized that the personas needed to not only describe children appropriately, but also capture differences in behaviours between cultures. 56 children aged 7–10 in the UK and India participated in the creation of personas of elementary school children, describing aspects such as school life, family life and technology use. A tool developed to evaluate personas demonstrated that both sets of children could individually create plausible personas, while content analysis of the personas demonstrated that children focused on behavioural and activity-based narratives that were similar between the two groups, with only limited cultural differences identified. The findings suggest that child-generated personas can be a viable method in the design process, and may offer insights that aid cross-cultural design.
FittsFarm: Comparing Children’s Drag-and-Drop Performance Using Finger and Stylus Input on Tablets

We used a two-dimensional Fitts’ law task to compare finger and stylus input with children when performing drag-and-drop tasks on a tablet. Twenty-eight children completed the study. Drag-and-drop performance was significantly better using a low-cost stylus compared to finger input. Throughput was $$9\%$$ higher for stylus input (2.55 bps) compared to finger input (2.34 bps). Error rates were 35% percent higher for finger input (12.6%) compared to stylus input (9.3%). Error rates approximately doubled with smaller targets. There was no significant difference observed for movement time between input methods. Findings indicate schools should consider providing children with a low-cost stylus for educational activities on tablets.
A Multi-User Encryption and Authentication System Based on Joint Transform Correlation

Optical image encryption technology based on joint transform correlation (JTC) has attracted increasing attention from researchers. We propose a multi-user secure communication system based on the special properties of JTC. Multiple users utilize their own fingerprints to encrypt the plain-text in the encryption process, and each user must be first authenticated at the beginning of the decryption process. Only users with legitimate identities can perform the decryption process, whereas unauthorized users with false identities cannot, so the security of the system is greatly improved. Furthermore, we propose a multi-user double-image encryption method, which can better meet the needs of different security environments. Considering the possibility of overlapping images at the output end, we prove that a JTC-based image encryption system can avoid overlapping images at the output when the distance between the input images is 3W (W is the side length of a square image), which provides a theoretical foundation for further research. Finally, a numerical simulation demonstrates the effectiveness and feasibility of the proposed idea.
CNN-based Object Detection on Low Precision Hardware: Racing Car Case Study

Increasing interest in deep learning and convolutional neural networks resulted in the last years in multiple techniques aiming to improve their accuracy, training speed, and inference speed. At the same time, their computational cost triggered the design of several dedicated hardware architectures, aiming to handle the elevated number of operations neural networks require with minimal power budget, often exploiting reduced precision arithmetic. In this case study, we analyzed how several techniques can be merged together in the design of a track detector for a self-driving racing car, illustrating a step-by-step procedure required to adapt several theoretical works to a real-world scenario. Compared with the best previous detector, the new Proteins cone detector is optimized for low-precision deep learning accelerators. It runs 50% faster on GPU than the previous detector and at a simulated 272.5 FPS on a 1 W ASIC or at 16.4 FPS on a 12 W FPGA and achieves a detection score 16% higher than the previous implementation.
Detecting Overfitting via Adversarial Examples

The repeated community-wide reuse of test sets in popular benchmark problems raises doubts about the credibility of reported test-error rates. Verifying whether a learned model is overfitted to a test set is challenging as independent test sets drawn from the same data distribution are usually unavailable, while other test sets may introduce a distribution shift. We propose a new hypothesis test that uses only the original test data to detect overfitting. It utilizes a new unbiased error estimate that is based on adversarial examples generated from the test data and importance weighting. Overfitting is detected if this error estimate is sufficiently different from the original test error rate. We develop a specialized variant of our test for multiclass image classification, and apply it to testing overfitting of recent models to the popular ImageNet benchmark. Our method correctly indicates overfitting of the trained model to the training set, but is not able to detect any overfitting to the test set, in line with other recent work on this topic.
Forward-Backward Decoding for Regularizing End-to-End TTS

Neural end-to-end TTS can generate very high-quality synthesized speech, and even close to human recording within similar domain text. However, it performs unsatisfactory when scaling it to challenging test sets. One concern is that the encoder-decoder with attention-based network adopts autoregressive generative sequence model with the limitation of exposure bias To address this issue, we propose two novel methods, which learn to predict future by improving agreement between forward and backward decoding sequence. The first one is achieved by introducing divergence regularization terms into model training objective to reduce the mismatch between two directional models, namely L2R and R2L (which generates targets from left-to-right and right-to-left, respectively). While the second one operates on decoder-level and exploits the future information during decoding. In addition, we employ a joint training strategy to allow forward and backward decoding to improve each other in an interactive process. Experimental results show our proposed methods especially the second one (bidirectional decoder regularization), leads a significantly improvement on both robustness and overall naturalness, as outperforming baseline (the revised version of Tacotron2) with a MOS gap of 0.14 in a challenging test, and achieving close to human quality (4.42 vs. 4.49 in MOS) on general test.
Towards Shared Autonomy Applications using Whole-body Control Formulations of Locomanipulation

While widely studied in robotics for decades, mobile manipulation has recently seen a surge in interest for industrial applications due to increasing demands on flexibility and agility alongside productivity, particularly in small and medium enterprises. However, most mobile manipulation solutions frequently decouple the navigation from the manipulation problem effectively performing fixed-base manipulation using a repositionable manipulator. This is not only inefficient, but moreover limits the range of applications and disregards the inherent redundancy of a mobile manipulation system. In this work, we introduce a high-performance omnidirectional mobile manipulation platform with integrated whole-body control, real-time collision-free whole-body motion planning, and perception. We demonstrate its capability along with application scenarios on technical demonstrators involving moving manipulation targets as well as whole-body manipulation in simulation and hardware experiments. Finally, we deploy and evaluate our solution in field trials on an industrial oil and gas training facility on a sensor placement and manipulation task.
Image Denoising Using Novel Social Grouping Optimization Algorithm with Transform Domain Technique

In recent days, image communication has evolved in many fields like medicine, entertainment, gaming, mail, etc. Thus, it is an immediate need to denoise the received image because noise that is added in the channel during communication alters or deteriorates information contained in the image. Any image processing techniques concerned with image denoising or image noise removal has to be started with the spatial domain and end with the transform domain. A lot of research was carried out in the spatial domain by modifying the performance of different image filters such as mean filters, median filters, Laplacian filters, etc. Recently much research was carried out in Transform techniques under the transform domain, with evolutionary computing tools (ECT). ECT has proven to be dominant when compared with traditional denoising techniques in combination with wavelets in the transform domain. In this article, the authors applied a novel ECT such as SGOA on the denoising problem for denoising monochrome as well as color images and performance for denoising was evaluated using several image quality metrics.
An efficient and adaptive data-hiding scheme based on secure random matrix

There is a vivid research on securely deliver a secret message by using a data hiding technique in digital images. However, most existing solutions of data-hiding need to encrypt secret data first, and embed the encrypt message into cover image, which is not promising any security once the embedding algorithm is leaked. In this paper, an efficient and adaptive data hiding scheme is presented which is based on a new reference matrix named secure reference matrix. The scheme is flexible enough to meet different data hiding capacities and image quality as needed, and we allow the reference matrix to be more secure since it is randomly generated each time and make it much harder for being attack with exhaustive manner by extending the possible solution. We give the detail protocol for our scheme and provide the practical test and performance analysis. Experimental results show that our scheme has a better flexibility and efficiency in embedding process, and the average value of Peak Signal to Noise Ratio (PSNR) for the stego-images which are generated by our scheme have a higher visual quality for different embedding capacities, and our scheme is more efficient compared with related work.
Fuzzy-Based Recommendation System for University Major Selection

The decision of choosing a university major is one of the most important decisions in every adult life. To make a suitable decision, a student needs an expert opinion, time, and effort. Therefore, a decision-making system should be developed in order to help prospective students to increase their educational outcome and productivity. In Saudi Arabia, each university requires specific criteria in order to accept students. These criteria are made based on two factors: 1) the outcome of student’s qualification exams and 2) overall high school grades. The student must take these calculations into consideration when selecting a major. Thus, in this paper, a Fuzzy-Based Recommendation System (FRS) is proposed to aid students in choosing a suitable major. This system designed using Fuzzy Expert System (FES). Additionally, a cluster-based preferences technique is implemented to obtain the student's preferred majors, using distance measurement. The system has been tested on fifteen prospective students to measure its accessibility. Results showed that students are stratified by the suggested majors that fell in line with their preferences.
Natural Language Generation of Explanations of Fuzzy Inference Decisions

As Artificial Intelligence and fuzzy systems are at the center of the emergence of advanced technologies such as autonomous vehicles or medical decision support systems, a problem of trust from a human point of view is strongly appearing. In this article, we tackle the problem of explanation of a fuzzy inference system decision in its entirety: from the conception of an algorithm that produces a textual explanation to its evaluation.We define a function which is able to associate to any activated fuzzy rule, the structure responsible of its activation degree. To assess our method, we defined a protocol to evaluate AIgenerated explanation, and made an experiment: explanations obtained from the classification of pastas. Despite limitations, the results show a good transparency of the reasoning, consistency and good global effectiveness in generated explanations.
Modeling of Fuzzy Rule-base Algorithm for the Time Dependent Traveling Salesman Problem

The Traveling Salesman Problem (TSP) is one of the most extensively studied NP-hard graph search problems. In the literature, there have been numerous published attempts, applying various approaches in order to find the optimum (least cost) or semi optimum solution. Time Dependent Traveling Salesman Problem (TD TSP) is one of the most sufficient extensions and modifications of the original TSP problem. In TD TSP the costs of edges between nodes varies, they are assigned higher cost in the traffic jam region, such as city center or during the rush hour periods. In this paper, we introduce an even more realistic approach, the 3FTD TSP (Triple Fuzzy Time Dependent Traveling Salesman Problem); a fuzzified model of the original TD TSP. The 3FTD TSP presents a variation of the TD TSP utilizing fuzzy values in the cost between two nodes (shops, cities, etc.), the geographical areas of the traffic jam region, and also the rush hour period. The goal is to give a practically useful and realistic alternative of the basic TD TSP problem. In order to calculate the (quasi-) optimum solution, the Discrete Bacterial Memetic Evolutionary Algorithm was used, since it has been proven to be rather efficient (and predictably) in a wide range of NP-hard problems, including the original TSP and the TD TSP as well. The results from the runs based on the extensions of the family of benchmarks generated from the original TD TSP benchmark data set showed rather good and credible initial results.