Reviewerly/exHarmony huggingface dataset:
Four Geometry Problems to Introduce Automated Deduction in Secondary Schools

The introduction of automated deduction systems in secondary schools face several bottlenecks, the absence of the subject of rigorous mathematical demonstrations in the curricula, the lack of knowledge by the teachers about the subject and the difficulty of tackling the task by automatic means.Despite those difficulties we claim that the subject of automated deduction in geometry can be introduced, by addressing it in particular cases: simple to manipulate by students and teachers and reasonably easy to be dealt by automatic deduction tools.The subject is discussed by addressing four secondary schools geometry problems: their rigorous proofs, visual proofs, numeric proofs, algebraic formal proofs, synthetic formal proofs, or the lack of them.For these problems we discuss a lesson plan to address them with the help of Information and Communications Technology, more specifically, automated deduction tools.
Boosted Decision Trees

Boosted decision trees are a very powerful machine learning technique. After introducing specific concepts of machine learning in the high-energy physics context and describing ways to quantify the performance and training quality of classifiers, decision trees are described. Some of their shortcomings are then mitigated with ensemble learning, using boosting algorithms, in particular AdaBoost and gradient boosting. Examples from high-energy physics and software used are also presented.
OSSDIP: Open Source Secure Data Infrastructure and Processes Supporting Data Visiting

Meeting the conflicting goals of protecting and maintaining control over sensitive data while also allowing access by third parties constitutes a significant challenge. Secure data infrastructures support data visiting in a highly controlled and monitored environment which, if properly set-up and operated, provide high security guarantees through a combination of technical, legal and procedural mechanisms. To ease the process of deploying such a secure data infrastructure, we present a detailed documentation of the architecture and processes of such an infrastructure and provide a pre-configured reference implementation based entirely on open source software that can be flexibly configured to meet differing security requirements and deployment scenarios. We combine mechanisms for data visiting on secured infrastructure components with optional components of data anonymization and fingerprinting, covered by extensive logging and monitoring functions and embedded in defined processes and contractual frameworks. The set-up is based upon the experience of operating such a secure infrastructure in the medical domain for almost ten years, addressing the emerging need to make such a solution available to a larger set of stakeholders. We show that our system significantly enhances data visiting, offers a higher level of data isolation and present our open source reference implementation thereof.
Taxonomy enrichment with text and graph vector representations

Knowledge graphs such as DBpedia, Freebase or Wikidata always contain a taxonomic backbone that allows the arrangement and structuring of various concepts in accordance with hypo-hypernym (“class-subclass”) relationship. With the rapid growth of lexical resources for specific domains, the problem of automatic extension of the existing knowledge bases with new words is becoming more and more widespread. In this paper, we address the problem of taxonomy enrichment which aims at adding new words to the existing taxonomy. We present a new method which allows achieving high results on this task with little effort. It uses the resources which exist for the majority of languages, making the method universal. We extend our method by incorporating deep representations of graph structures like node2vec, Poincaré embeddings, GCN etc. that have recently demonstrated promising results on various NLP tasks. Furthermore, combining these representations with word embeddings allows us to beat the state of the art. We conduct a comprehensive study of the existing approaches to taxonomy enrichment based on word and graph vector representations and their fusion approaches. We also explore the ways of using deep learning architectures to extend taxonomic backbones of knowledge graphs. We create a number of datasets for taxonomy extension for English and Russian. We achieve state-of-the-art results across different datasets and provide an in-depth error analysis of mistakes.
Enriching Everyday Lived Experiences in Dementia Care

Research in HCI is increasingly investigating the role of technology in supporting meaningful and social activities to enhance the lived experiences of people with dementia. However, to further enrich the daily experiences in care, more insight is needed into how technology can directly promote social participation and pleasurable experiences in everyday care situations. This paper discusses the deployment of VITA and SAM: two research products that address the social and emotional needs of residents in day-to-day dementia care. We report how both products offered aesthetic and sensory enrichment, created new experiences in the everyday, and were integrated into the care environment. Furthermore, we identify implications for design to provide: 1) aesthetics in care, 2) authentic experiences, 3) reinforcing everyday life, and 4) community-driven use in practice. We contribute to existing research by demonstrating how technology for dementia care can transcend instrumental use and culminate in warm-felt everyday experiences.
Analysis of Factors Affecting Learning Difficulties during the Covid 19 Pandemic

The purpose of the study was to determine the factors that influence learning difficulties during the Covid 19 pandemic in junior high and high school age adolescents in the Makassar area of ??East Jakarta. The research method used in this study is a qualitative method by analyzing the data from observations and interviews. The analytical technique used in this research is qualitative data analysis. The object of this research is junior high school and high school youth in Makassar, East Jakarta, with a total of 10 informants including 2 junior high school students and 8 high school students. Based on the research conducted, it is identified the factors that influence learning difficulties in the pandemic era in junior high and high school age adolescents in the Makassar area of ??East Jakarta including the difficulty of understanding material through distance learning, low interest in learning, difficult social adjustment, poor health. not good, lack of learning facilities, limited study space, materials
Creation of high-dimensional entanglement of polar molecules via optimal control fields

Quantum entanglement in high-dimensional Hilbert space offers broad prospects for quantum information science. Polar molecules have a rich internal structure and long coherence time, serving as an attractive candidate for quantum information processing. In this paper, we propose a theoretical scheme for creating high-dimensional entangled states based on ultracold SrO molecules. Qudits are encoded in the pendular states induced by an external electric field and coupled by the dipole-dipole interaction. With the assistance of optimal control theory, a series of optimal microwave fields is designed for the generation of an entangled qutrit-qubit state, qutrit-qutrit state, and ququart-ququart state through numerical iterations. We detail the relation of the fidelity and entanglement of the systemic final states with the number of iteration steps after the control fields are applied and analyze the population dynamics of the field-driven wave functions during time evolution. Moreover, three coupled SrO molecules arranged in an equilateral triangle configuration are employed as pendular qubits, and the trimolecular $W$ and Greenberger-Horne-Zeilinger states are realized with high fidelities by optimal control. The specific molecule and experimental parameters used in our theoretical studies are chosen for computational convenience, but we include a discussion of how our results can be applied to realistic situations as well. In principle, our results could be extended to a situation with more pendular qudits, providing a significant step toward the achievement of high-dimensional quantum information processing with arrays of polar molecules.
Decomposition and composition of deep convolutional neural networks and training acceleration via sub-network transfer learning

Deep convolutional neural network (DCNN) has led to significant breakthroughs in deep learning.However, larger models and larger datasets result in longer training times slowing down the development progress of deep learning.In this paper, following the idea of domain decomposition methods, we propose and study a new method to parallelize the training of DCNNs by decomposing and composing DCNNs.First, a global network is decomposed into several sub-networks by partitioning the width of the network (i.e., along the channel dimension) while keeping the depth constant.All the sub-networks are individually trained, in parallel without any interprocessor communication, with the corresponding decomposed samples from the input data.Then, following the idea of nonlinear preconditioning, we propose a sub-network transfer learning strategy in which the weights of the trained sub-networks are recomposed to initialize the global network, which is then trained to further adapt the parameters.Some theoretical analyses are provided to show the effectiveness of the sub-network transfer learning strategy.More precisely speaking, we prove that (1) the initialized global network can extract the feature maps learned by the sub-networks; (2) the initialization of the global network can provide an upper bound and a lower bound for the cost function and the classification accuracy with the corresponding values of the trained sub-networks.Some experiments are provided to evaluate the proposed methods.The results show that the sub-network transfer learning strategy can indeed provide good initialization and accelerate the training of the global network.Additionally, after further training, the transfer learning strategy shows almost no loss of accuracy and sometimes the accuracy is higher than if the network is initialized randomly.
An Ultra-Fast Authenticated Encryption Scheme with Associated Data Using AES-OTR

Applications relying on the Ethernet IEEE 802.3ba and IEEE802.3b standards require secure data encryption and authentication at extremely high speeds (at least 100 Gbit/s). The Galois/Counter Mode (GCM) is currently considered the de facto standard for hardware high-speed authenticated encryption, although other algorithms have been proposed in the literature such as the Offset Codebook Mode (OCB). The challenge in terms of providing security for high-speed applications is to achieve implementations that explore the parallelism of these algorithms; however, this translates into area cost. In this work, we propose an alternative to GCM and OCB. We show that a combination of the Offset Two-Round authenticated-encryption scheme with the AES block cipher (known as AES-OTR) is exceptionally well suited for exploiting fine-grained parallelism, and can therefore be used to achieve ultra-high-speed data encryption rates. The experiments reported in this paper show that our pipeline-parallel implementation of AES-OTR outperforms the GCM and OCB schemes in terms of throughput per area while using almost half of the logic resources. Our implementation used a Stratix 4 FPGA device as well as several devices from the Virtex family. Implementations of AES-OTR on Stratix 4 used 11kALMs and achieved a throughput of 143.65 Gbit/s. On the Virtex Ultrascale, our design used 31,859 LUTs with a throughput of 204.92 Gbit/s.
CLC: A Consensus-based Label Correction Approach in Federated Learning

Federated learning (FL) is a novel distributed learning framework where multiple participants collaboratively train a global model without sharing any raw data to preserve privacy. However, data quality may vary among the participants, the most typical of which is label noise. The incorrect label would significantly damage the performance of the global model. In FL, the inaccessibility of raw data makes this issue more challenging. Previously published studies are limited to using a task-specific benchmark-trained model to evaluate the relevance between the benchmark dataset in the server and the local one on the participants’ side. However, such approaches have failed to exploit the cooperative nature of FL itself and are not practical. This paper proposes a Consensus-based Label Correction approach (CLC) in FL, which tries to correct the noisy labels using the developed consensus method among the FL participants. The consensus-defined class-wise information is used to identify the noisy labels and correct them with pseudo-labels. Extensive experiments are conducted on several public datasets in various settings. The experimental results prove the advantage over the state-of-art methods. The link to the source code is https://github.com/bixiao-zeng/CLC.git .
Active intellectual property protection for deep neural networks through stealthy backdoor and users’ identities authentication

Recently, the intellectual properties (IP) protection of deep neural networks (DNN) has attracted serious concerns. A number of DNN copyright protection methods have been proposed. However, most of the existing DNN watermarking methods can only verify the ownership of the model after the piracy occurs, which cannot actively prevent the occurrence of the piracy and do not support users' identities management, thus can not satisfy the requirements of commercial DNN copyright management. In addition, the query modification attack which was proposed recently can invalidate most of the existing backdoor-based DNN watermarking methods. In this paper, we propose an active intellectual properties protection technique for DNN models via stealthy backdoor and users' identities authentication. For the first time, we use a set of clean images (as the watermark key samples) to embed an additional class into the DNN for ownership verification, and use the image steganography to embed users' identity information into these watermark key images. Each user will be assigned with a unique identity image for identity authentication and authorization control. Since the backdoor instances are clean images outside the dataset, the backdoor trigger is visually imperceptible and concealed. In addition, we embed the watermark by exploiting an additional class outside the main tasks, which establishes a strong connection for watermark key samples and the corresponding label. As a result, the proposed method is concealed, robust, and can resist common attacks and query modification attack. Experimental results demonstrate that, the proposed method can obtain 100% watermark accuracy and 100% fingerprint authentication success rate on Fashion-MNIST and CIFAR-10 datasets. In addition, the proposed method is demonstrated to be robust against the model fine-tuning attack, model pruning attack, and query modification attack. Compared with three existing DNN watermarking methods, the proposed method has better performance on watermark accuracy and robustness against the query modification attack.
Synthetic Traffic Sign Image Generation Applying Generative Adversarial Networks

Recently, it was shown that convolutional neural networks (CNNs) with suitably annotated training data and results produce the best traffic sign detection (TSD) and recognition (TSR). The whole system’s efficiency is determined by the data collecting process based on neural networks. As a result, the datasets for traffic signs in most nations throughout the globe are difficult to recognize because of their diversity. To address this problem, we must create a synthetic image to enhance our dataset. We apply deep convolutional generative adversarial networks (DCGAN) and Wasserstein generative adversarial networks (Wasserstein GAN, WGAN) to generate realistic and diverse additional training images to compensate for the original image distribution’s data shortage. This study focuses on the consistency of DCGAN and WGAN images created with varied settings. We utilize an actual picture with various numbers and scales for training. Additionally, the Structural Similarity Index (SSIM) and the Mean Square Error (MSE) were used to determine the image’s quality. In our study, we computed the SSIM values between pictures and their corresponding real images. When more training images are used, the images created have a significant degree of similarity to the original image. The results of our experiment reveal that the most leading SSIM values are achieved when 200 total images of [Formula: see text] pixels are utilized as input and the epoch is 2000.
The role of value added across economic sectors in modulating the effects of FDI on TFP and economic growth dynamics

Purpose This study investigates (1) the effect of foreign direct investment (FDI) on total factor productivity (TFP) and economic growth dynamics and (2) the relevance of value added from three economic sectors in modulating the established effect of FDI on TFP and economic growth dynamics. Design/methodology/approach The geographical and temporal scopes are respectively 25 Sub-Saharan African countries and the period 1980–2014. The empirical evidence is based on non-interactive and interactive generalised method of moments. Findings The following main findings are established. First, FDI has a positive effect on gross domestic product (GDP) growth, GDP per capita and welfare real TFP. Second, the effect of FDI is negative on real GDP and TFP while the impact is insignificant on real TFP growth and welfare TFP. Third, values added to the three economic sectors largely modulate FDI to produce negative net effects on TFP and growth dynamics. Practical implications Policy implications are discussed with particular emphasis on the need to complement added value across various economic sectors in order to leverage on the benefits of FDI in TFP and economic growth. Originality/value To the best of the authors’ knowledge, this is the first study to assess how value added from various economic sectors affect the relevance of FDI on macroeconomic outcomes.
The benefits of server hinting when DASHing or HLSing

Streaming clients almost always compete for the available bandwidth and server capacity. Not every client's playback buffer conditions will be the same, though, nor should be the priority with which the server processes the individual requests coming from these clients. In an earlier work, we demonstrated that if clients conveyed their buffer statuses to the server using a Common Media Client Data (CMCD) query argument, the server could allocate its output capacity among all the requests more wisely, which could significantly reduce the rebufferings experienced by the clients.
Fleche

Deep learning based models have dominated current production recommendation systems. However, the gap between CPU-side DRAM data accessing and GPU processing still impedes their inference performance. GPU-resident cache can bridge this gap, but we find that existing systems leave the benefits to cache the embedding table, a huge sparse structure, on GPU unexploited. In this paper, we present Fleche, a holistic cache scheme with detailed designs for efficient GPU-resident embedding caching. Fleche (1) uses one cache backend for all embedding tables to improve the total cache utilization, and (2) merges small kernel calls into one unitary call to reduce the overhead of kernel maintenance (e.g., kernel launching and synchronizing). Furthermore, we carefully design the cache query workflow for finer-grain parallelism. Evaluations with real-world datasets show that compared with the prior art, Fleche significantly improves the throughput of embedding layer by 2.0 -- 5.4×, and gets up to 2.4× speedup of end-to-end inference throughput.
Gesturenomy: Touchless Restaurant Menu Using Hand Gesture Recognition

Hand gesture recognition is one of the more modern methods of human-computer interaction.However, study of its application in real world setting is sparse, especially its use in digital restaurant menus.We discuss various types of computer vision-based hand gesture recognition and decide on hand pose estimation as the method of recognition used.An analysis into the requirements show that respondents are concerned with the hygiene of touchscreen digital menus.We provide a description of the system being developed.The system passes the user acceptance tests given.
ELUNet: an efficient and lightweight U-shape network for real-time semantic segmentation

The demand to design lightweight semantic segmentation models on mobile devices is growing. Current U-shape structures can improve the segmentation accuracy. However, they can hardly achieve lightweight requirements due to their inefficient encoders. Besides, partial details and edges are damaged during the process of repeated downsampling. To this end, we propose an efficient and lightweight U-shape network (ELUNet) for real-time semantic segmentation. In the encoder, a light split-shuffle convolution block is designed as the key component of feature extraction to achieve high-precision segmentation in the resource-limited scene. Furthermore, we propose a bridge channel attention module in the skip connection to selectively emphasize the valuable features. In the decoder, we propose an upsample feature fusion module to capture global contextual information, significantly improving the ability of the network to extract spatial information. Moreover, we design an edge refinement module to refine the segmentation predictions further. Extensive experiments prove the effectiveness of the ELUNet on Cityscapes and Camvid benchmarks. Specifically, the ELUNet contains only 2.0M parameters and achieves 73.3% mIoU on Cityscapes validation set with the speed of 52.6 FPS for a 512 × 1024 input image on a single 1080Ti GPU.
Counterfactual Models for Fair and Adequate Explanations

Recent efforts have uncovered various methods for providing explanations that can help interpret the behavior of machine learning programs. Exact explanations with a rigorous logical foundation provide valid and complete explanations, but they have an epistemological problem: they are often too complex for humans to understand and too expensive to compute even with automated reasoning methods. Interpretability requires good explanations that humans can grasp and can compute. We take an important step toward specifying what good explanations are by analyzing the epistemically accessible and pragmatic aspects of explanations. We characterize sufficiently good, or fair and adequate, explanations in terms of counterfactuals and what we call the conundra of the explainee, the agent that requested the explanation. We provide a correspondence between logical and mathematical formulations for counterfactuals to examine the partiality of counterfactual explanations that can hide biases; we define fair and adequate explanations in such a setting. We provide formal results about the algorithmic complexity of fair and adequate explanations. We then detail two sophisticated counterfactual models, one based on causal graphs, and one based on transport theories. We show transport based models have several theoretical advantages over the competition as explanation frameworks for machine learning algorithms.