Reviewerly/exHarmony huggingface dataset:
Developing memory-based models of ACT-R within a statistical framework

The ACT-R cognitive architecture is a computational framework for developing, simulating and testing comprehensive theories of cognition. By far, the most common method of evaluating ACT-R models involves generating predictions through Monte Carlo simulation and comparing those predictions to aggregated human data. This approach has several limitations, including computational inefficiency, the potential for averaging artifacts, and difficulty representing uncertainty in parameter estimates. In this paper, we demonstrate the fundamentals of developing models of ACT-R within a Bayesian framework. Instantiating ACT-R in a Bayesian framework has many advantages, including the ability to use modern parameter estimation and model comparison techniques, the ability to compare ACT-R to other closed-form models, increased computational efficiency, and the ability to perform a deeper mathematical analysis of model properties. We develop model variants of the classic fan experiment, beginning with a simple baseline model of ACT-R’s declarative memory system and progressing through increasingly complex variants until reaching a moderately complex general model. Our hope is that this will highlight connections between computational and mathematical approaches to formal modeling and facilitate new and exciting research.
Quantum geometric machine learning for quantum circuits and control

Abstract The application of machine learning techniques to solve problems in quantum control together with established geometric methods for solving optimisation problems leads naturally to an exploration of how machine learning approaches can be used to enhance geometric approaches for solving problems in quantum information processing. In this work, we review and extend the application of deep learning to quantum geometric control problems. Specifically, we demonstrate enhancements in time-optimal control in the context of quantum circuit synthesis problems by applying novel deep learning algorithms in order to approximate geodesics (and thus minimal circuits) along Lie group manifolds relevant to low-dimensional multi-qubit systems, such as SU(2), SU(4) and SU(8). We demonstrate the superior performance of greybox models, which combine traditional blackbox algorithms with whitebox models (which encode prior domain knowledge of quantum mechanics), as means of learning underlying quantum circuit distributions of interest. Our results demonstrate how geometric control techniques can be used to both (a) verify the extent to which geometrically synthesised quantum circuits lie along geodesic, and thus time-optimal, routes and (b) synthesise those circuits. Our results are of interest to researchers in quantum control and quantum information theory seeking to combine machine learning and geometric techniques for time-optimal control problems.
DWS-MKL: Depth-width-scaling multiple kernel learning for data classification

Deep learning technologies have been rapidly developing recently. They have shown excellent performances in many fields. However, deep learning networks have weak adaptability to small sample sizes.Usually, deep learning networks require tens of thousands of samples to avoid overfitting. In contrast, the kernel method can deal with small sample sizes. In this paper, we use only dozens of samples to train a successful classifier. Deep multiple kernel learning (DMKL) has emerged in recent years. It combines the ideas of deep learning and multiple kernel learning. Unfortunately, the DMKL network with a fixed structure cannot adapt to data of different dimensions and sizes. Therefore, in this paper, we propose a novel depth-width-scaling multiple kernel learning (DWS-MKL) algorithm. It has the ability to adjust the architecture according to the input data. We optimize the estimation of leave-one-out error by using the span bound instead of the dual objective function. Finally, we choose a large number of data sets from the UCI benchmark data sets for classification tasks. We also evaluate DWS-MKL on the MNIST data set. The experimental results show that different frameworks have different performances on the same dataset. Our DWS-MKL algorithm obtains better classification results than the state-of-the-art kernel learning algorithms, as determined by a thorough comparison. The encouraging results demonstrate that our method has the potential to improve generalization performance of the model.
Binary Galois field extensions dependent multimedia data security scheme

Finite fields are widely used in modern cryptographic architecture. The prominent finite field based symmetric and asymmetric cryptosystems are (ECC) elliptic curve cryptography, RSA, (AES) advanced encryption standard and pairing-based cryptography. The arithmetic operations of a finite field performed efficiently, that meet the design space constraints and execution speed. These aims resolve tremendous challenges, which required interdisciplinary efforts to render the best algorithms, implementations, architectures, and design practices. This study proposes convenient finite field arithmetic based symmetric key cryptosystem for multimedia data security. The scheme is comprised of diffusion and confusion. To execute the diffusion phase the random numbers generation is essential in utilizing the multiplication and inversion operations over the binary Galois field extensions. The main objective of using these operations is to establish substantial randomness with less computational exertions. In addition, a block cipher construction mechanism has been deployed for the confusion phase of the algorithm which generates multiple S-boxes with fewer rounds as compared to the customary block ciphers. Therefore, efficient creation of confusion in multimedia data and hence more security is attained. The experimental results and the time complexity manifest the efficiency of the scheme against various attacks. Also, as a result of a fast and simple implementation of the binary finite field in hardware and software, the proposed cryptosystem is more convenient to implement as compared to the prevailing schemes.
Strong Social Graph Based Trust-Oriented Graph Pattern Matching With Multiple Constraints

Online social network is popular and graph pattern matching (GPM) has been significant in many social network based applications, such as experts finding and social position detection. However, the existing GPM methods do not consider the multiple constraints of the social contexts in GPM, which are commonly found in various applications, or they do not consider the changes of graph structure in the index maintenance of GPM, leading to low efficiency. In this paper, we first propose a multi-constrained simulation based on the bounded graph simulation, and propose a multi-constrained graph pattern matching (MC-GPM) problem. To improve the efficiency of MC-GPM in large social graphs, we propose a new concept, strong social graph (SSG), that contains the users who have strong social connections. Then, we propose an SSG-index method to index the reachability, the graph patterns, and the social contexts of social graphs. Finally, we propose an incremental algorithm to maintain the SSG-index, which can greatly save the execution time when faced with the change of the structures of SSGs. Moreover, by combining SSG-index, we develop a heuristic algorithm, called SSG-MGPM, to identify MC-GPM results effectively and efficiently. An empirical study over five real-world social graphs has demonstrated the superiority of our approach in terms of efficiency and effectiveness.
On the Automatic Exploration of Weight Sharing for Deep Neural Network Compression

Deep neural networks demonstrate impressive levels of performance, particularly in computer vision and speech recognition. However, the computational workload and associated storage inhibit their potential in resource-limited embedded systems. The approximate computing paradigm has been widely explored in the literature. It improves performance and energy-efficiency by relaxing the need for fully accurate operations. There are a large number of implementation options with very different approximation strategies (such as pruning, quantization, low-rank factorization, knowledge distillation, etc.). To the best of our knowledge, no automated approach exists to explore, select and generate the best approximate versions of a given convolutional neural network (CNN) according to the design objectives. The goal of this work in progress is to demonstrate that the design space exploration phase can enable significant network compression without noticeable accuracy loss. We demonstrate this via an example based on weight sharing and show that our method can obtain a 4x compression rate in an int-16 version of LeNet-5 (5-layer 1,720-kbit CNNs) without re-training and without any accuracy loss.
Investigating mechanical damage mechanisms of tapestries displayed at different angles using 2D DIC

Abstract Tapestries represent a key but fragile component of many historic collections, as well as a complex challenge for textile conservators who aim to ensure their preservation. Indeed, the heterogeneity of materials, weaving features and conservation history make it difficult to predict the mechanical and physical behaviour of these objects, especially when hung for their display. The reported research postulates and investigates the role of different factors, namely relative humidity related strains and creep, in promoting the mechanical deterioration of tapestries. To track strain, the contactless full-field optical technique digital image correlation (DIC) was used. In particular, 2D DIC was successfully employed to monitor strains across large areas of a tapestry, demonstrating the occurrence of fatigue and creep damage mechanisms. While the former had the most influence on overall expansions and contractions, creep affected damaged areas. In addition, DIC was used for a preliminary evaluation of the efficacy of sloping boards, a non-traditional display method chosen by an increasing number of European museums in an attempt to reduce strain across tapestries. Both theoretical analysis and experimental work showed that, when minimal friction is present, small angles, like 5° from the vertical, do not appear to promote noticeable reductions in strain.
Controlling Mouse Motions Using Eye Tracking Using Computer Vision

This paper introduces an algorithm to carry out the functions of a mouse by providing a hands-free interaction between humans and computers. It provi des an alternative to the traditional mouse computer. By using different expressions of a face using computer vision and matching it with already stored expression and execute actions as per the move. This algorithm will help physically disabled people to perform the functions of a mouse using their face and eye movement. It allows them to left-click, right-click, scroll up and down, move the cursor up, down, left, right. The system has a very basic need like webcam, NumPy, dlib, and a few other basic libraries.
Light-field-depth-estimation network based on epipolar geometry and image segmentation

In this paper, we propose a convolutional neural network based on epipolar geometry and image segmentation for light-field depth estimation. Epipolar geometry is utilized to estimate the initial disparity map. Multi-orientation epipolar images are selected as input data, and the convolutional blocks are adopted based on the disparity of different-direction epipolar images. Image segmentation is used to obtain the edge information of the central sub-aperture image. By concatenating the output of the two parts, an accurate depth map could be generated with fast speed. Our method achieves a high rank on most quality assessment metrics in the HCI 4D Light Field Benchmark and also shows effectiveness in estimating accurate depth on real-world light-field images.
MPC with Friends and Foes

Classical definitions for secure multiparty computation assume the existence of a single adversarial entity controlling the set of corrupted parties. Intuitively, the definition requires that the view of the adversary, corrupting t parties, in a real-world execution can be simulated by an adversary in an ideal model, where parties interact only via a trusted-party. No restrictions, however, are imposed on the view of honest parties in the protocol, thus, if honest parties obtain information about the private inputs of other honest parties – it is not counted as a violation of privacy. This is arguably undesirable in many situations that fall into the MPC framework. Nevertheless, there are secure protocols (e.g., the 2-round multiparty protocol of Ishai et al. [CRYPTO 2010] tolerating a single corrupted party) that instruct the honest parties to reveal their private inputs to all other honest parties (once the malicious party is somehow identified). In this paper, we put forth a new security notion, which we call FaF-security, extending the classical notion. In essence, $$(t,h^*)$$ -FaF-security requires the view of a subset of up to $$h^*$$ honest parties to also be simulatable in the ideal model (in addition to the view of the malicious adversary, corrupting up to t parties). This property should still hold, even if the adversary leaks information to honest parties by sending them non-prescribed messages. We provide a thorough exploration of the new notion, investigating it in relation to a variety of existing security notions. We further investigate the feasibility of achieving FaF-security and show that every functionality can be computed with (computational) $$(t,h^*)$$ -FaF full-security, if and only if $$2t+ h^*<m$$ . Interestingly, the lower-bound result actually shows that even fair FaF-security is impossible in general when $$2t\,+\,h^*\ge m$$ (surprisingly, the view of the malicious attacker is not used as the trigger for the attack). We also investigate the optimal round complexity for $$(t,h^*)$$ -FaF-secure protocols and give evidence that the leakage of private inputs of honest parties in the protocol of Ishai et al. [CRYPTO 2010] is inherent.
SoftRec: Multi-Relationship Fused Software Developer Recommendation

Collaboration efficiency is of primary importance in software development. It is widely recognized that choosing suitable developers is an efficient and effective practice for improving the efficiency of software development and collaboration. Recommending suitable developers is complex and time-consuming due to the difficulty of learning developers’ expertise and willingness. Existing works focus on learning developers’ expertise and interactions from their explicit historical information and matching them to specific task. However, such procedures may suffer low accuracy because they ignore implicit information, such as (1) developer–developer collaboration relationships, (2) developer–task implicit interaction relationships, and (3) task–task association relationships, etc. To that end, this paper proposes a multi-relationship fused approach for software developer recommendation (termed SoftRec). First, in addition to explicit developer–task interactions, it considers multivariate implicit relationships, including the three types mentioned above. Second, it integrates these relationships based on joint matrix factorization and generates forecast results upon the architecture of deep neural network. Furthermore, we propose a fast update method to address the cold start issue by making online recommendations for new developers and new tasks. Extensive experiments are conducted on two real-world datasets, and a user study is conducted in a well-known software company. The results demonstrate that SoftRec outperforms four state-of-the-art works.
Adaptive Margin Based Deep Adversarial Metric Learning

In the past decades, learning an effective distance metric between pairs of instances has played an important role in the classification and retrieval task, for example, the person identification or malware retrieval in the IoT service. The core motivation of recent efforts focus on improving the metric forms, and already showed promising results on the various applications. However, such models often fail to produce a reliable metric on the ambiguous test set. It happens mainly due to the sampling process of the training set, which is not representative of the distribution of the negative samples, especially the examples that are closer to the boundary of different categories (also called hard negative samples). In this paper, we focus on addressing such problems and propose an adaptive margin deep adversarial metric learning (AMDAML) framework. It exploits numerous common negative samples to generate potential hard (adversarial) negatives and applies them to facilitate robust metric learning. Apart from the previous approaches that typically depend on the search or data augmentation to find hard negative samples, the generation of adversarial negative instances could avoid the limitation of domain knowledge and constraint pairs’ amount. Specifically, in order to prevent over fitting or underfitting during the training step, we propose an adaptive margin loss that preserves a flexible margin between the negative (include the adversarial and original) and positive samples. We simultaneously train both the adversarial negative generator and conventional metric objective in an adversarial manner and learn the feature representations that are more precise and robust. The experimental results on practical data sets clearly demonstrate the superiority of AMDAML to representative state-of-the-art metric learning models.
CMAL: Cost-Effective Multi-Label Active Learning by Querying Subexamples

Multi-label active learning (MAL) aims to learn an accurate multi-label classifier by selecting which examples (or example-label pairs) will be annotated and reducing query effort. MAL is a more complicated and expensive process than single-label active learning, due to one example can be associated with a set of non-exclusive labels and the annotator has to scrutinize the whole example and label space to provide correct annotations. Instead of scrutinizing the whole example for annotation, we may just examine some of its subexamples with respect to a label for annotation. In this way, we can not only save the annotation cost but also speedup the annotation process. Given this observation, we introduce CMAL, a two-stage Cost-effective MAL strategy (CMAL) by querying subexamples. CMAL first selects the most informative example-label pairs by leveraging uncertainty, label correlation and label space sparsity. Specifically, the uncertainty of a label to an example can be reduced if its correlated labels already annotated to the example, and its uncertainty can be reduced also if more examples annotated to this label. Next, CMAL greedily queries the most probable positive subexample-label pairs of the selected example-label pair. In addition, we propose rCMAL to account for the representative of examples to more reliably select example-label pairs in the first stage. Extensive experiments on multi-label datasets from diverse domains show that our proposed CMAL and rCMAL can better save the query cost than state-of-the-art MAL methods. The contribution of leveraging label correlation, label sparsity, and representative for saving cost is also confirmed.
Research on Route Planning of AUV Based on Genetic Algorithms

In order to solve the problem of premature and long search time of traditional genetic algorithm, the paper proposes an improved genetic algorithm and builds a route planning model based on the improved genetic algorithm. The paper not only adjusts the crossover probability and mutation probability adaptively, but also changes the evaluation criteria of population diversity, where the premature problem of simple genetic algorithm is solved. In addition, a model of obstacle avoidance is established to ensure that there is no collision in the whole route planning. Finally, combined with the AUV path planning problem, it is verified that the improved genetic algorithm model can not only solve the problem of premature algorithm, ensure that AUV and obstacles do not collide, but also significantly shorten the route planning time and the path length.
Virtual Reality Aids Game Navigation: Evidence from the Hypertext Lostness Measure

Instead of traditional free movement, node-based movement can be used in virtual reality (VR) games. In node-based movement systems, players navigate by jumping to set locations. Node-based movement is similar to hypertext navigation. We show that the hypertext lostness measure can be used as a game analytic to evaluate navigational efficiency. In a randomized controlled trial with 25 adolescent participants, an immersive desktop game environment and a VR game environment were compared on the transmission of in-game educational content and navigational efficiency. Results show that the hypertext lostness measure is also valuable outside its original hypertext domain: in VR. VR did not improve players' retention of factual knowledge, but did significantly improve players' spatial knowledge and navigational efficiency. We conclude (a) the hypertext lostness measure is also valuable for node-based VR games and (b) VR games add to spatial learning, even when compared with already immersive desktop games.
Significance of spectral cues in automatic speech segmentation for Indian language speech synthesizers

Building speech synthesis systems for Indian languages is challenging owing to the fact that digital resources for these languages are hardly available. Vocabulary independent speech synthesis requires that a given text is split at the level of the smallest sound unit, namely, phone. The waveforms or models of phones are concatenated to produce speech. The waveforms corresponding to that of the phones are obtained manual (listening and marking) when digital resources are scarce. But the manual labeling of speech data (also known as speech segmentation) can lead to inconsistencies as the duration of phones can be as short as 10ms. The most common approach to automatic segmentation of speech is to perform forced alignment using monophone hidden Markov models (HMMs) that have been obtained using embedded re-estimation after flat start initialization. These results are then used in neural network frameworks to build better acoustic models for speech synthesis/recognition. Segmentation using this approach requires large amounts of data and does not work very well for low resource languages. To address the issue of paucity of data, signal processing cues like short-term energy (STE) and sub-band spectral flux (SBSF) are used in tandem with HMM based forced alignment for automatic speech segmentation. STE and SBSF are computed on the speech waveforms. STE yields syllable boundaries, while SBSF provides locations of significant change in spectral flux that are indicative of fricatives, affricates, and nasals. STE and SBSF cannot be used directly to segment an utterance. Minimum phase group delay based smoothing is performed to preserve these landmarks, while at the same time reducing the local fluctuations. The boundaries obtained with HMMs are corrected at the syllable level, wherever it is known that the syllable boundaries are correct. Embedded re-estimation of monophone HMM models is again performed using the corrected alignment. Thus, using signal processing cues and HMM re-estimation in tandem, robust monophone HMM models are built. These models are then used in Gaussian mixture model (GMM), deep neural network (DNN) and convolutional neural network (CNN) frameworks to obtain state-level frame posteriors. The boundaries are again iteratively corrected and re-estimated. Text-to-speech (TTS) systems are built for different Indian languages using phone alignments obtained with and without the use of signal processing based boundary corrections. Unit selection based and statistical parametric based TTS systems are built. The result of the listening tests showed a significant improvement in the quality of synthesis with the use of signal processing based boundary correction.