Reviewerly/exHarmony huggingface dataset:
Reconfigured VLSI Architecture for Discrete Wavelet Transform

This paper presents reconfigured dual-memory controller-based VLSI architecture for discrete wavelet transform to meet the wide variety of diverse computing requirements of the future generation system on chip designs. The proposed architecture mainly consists of a reconfigured DWT processor with dedicated memory which enhances the overall performance of the design. Generally any image and video processing algorithm memory plays are major criteria in determining the overall performance of the design. We have designed and implemented a dual-memory controller DWT processor on ZedBoard. This created dual-memory controlled DWT IP can be reconfigured as per designer requirement for high-end application. The DWT is applied to various images, and the compression ratio is obtained. The above architecture is implemented in SoC XC7Z020 FPGA, and the results show that architecture has reduced memory, low power consumption, and high throughput over several designs. The performance is compared with the available architectures showing good agreements. The area utilized by the DWT architecture is 13%, the delay is 11.577 ns, and the total on-chip power consumption is 23.8 mW. The number of slice LUTs and slice registers utilized for the design is 912 and 1469, respectively.
Radial and Directional Posteriors for Bayesian Neural Networks

We propose a new variational family for Bayesian neural networks. We decompose the variational posterior into two components, where the radial component captures the strength of each neuron in terms of its magnitude; while the directional component captures the statistical dependencies among the weight parameters. The dependencies learned via the directional density provide better modeling performance compared to the widely-used Gaussian mean-field-type variational family. In addition, the strength of input and output neurons learned via the radial density provides a structured way to compress neural networks. Indeed, experiments show that our variational family improves predictive performance and yields compressed networks simultaneously.
Optimal entanglement witnesses from limited local measurements

We address the problem of optimizing entanglement witnesses when a limited fixed set of local measurements can be performed on a bipartite system, thus providing a procedure, feasible also for experiments, to detect entangled states using only the statistics of these local measurements. We completely characterize the class of entanglement witnesses of the form $W={P}^{\mathrm{\ensuremath{\Gamma}}}$, where $\mathrm{\ensuremath{\Gamma}}$ denotes partial transposition, that can be constructed from the measurements of the bipartite operators ${\ensuremath{\sigma}}_{x}\ensuremath{\bigotimes}{\ensuremath{\sigma}}_{x}, {\ensuremath{\sigma}}_{y}\ensuremath{\bigotimes}{\ensuremath{\sigma}}_{y}$, and ${\ensuremath{\sigma}}_{z}\ensuremath{\bigotimes}{\ensuremath{\sigma}}_{z}$ in the case of two-qubit systems. In particular, we consider all possible extremal decomposable witnesses within the considered class that can be defined from this set of measurements. Finally, we discuss possible extensions to higher-dimension bipartite systems when the set of available measurements is characterized by the generalized Gell-Mann matrices. We provide several examples of entanglement witnesses, both decomposable and indecomposable, that can be constructed with these limited resources.
Cooperative coevolution of real predator robots and virtual robots in the pursuit domain

The pursuit domain, or predator–prey problem is a standard testbed for the study of coordination techniques. In spite that its problem setup is apparently simple, it is challenging for the research of the emerged swarm intelligence. This paper presents a particle swarm optimization (PSO) based cooperative coevolutionary algorithm for the (predator) robots, called CCPSO-R, where real and virtual robots coexist in an evolutionary algorithm (EA). Virtual robots sample and explore the vicinity of the corresponding real robots and act as their action spaces, while the real robots consist of the real predators who actually pursue the prey robot without fixed behavior rules under the immediate guidance of the fitness function, which is designed in a modular manner with very limited domain knowledge. In addition, kinematic limits and collision avoidance considerations are integrated into the update rules of robots. Experiments are conducted on a scalable swarm of predator robots with 4 types of preys, the results of which show the reliability, generality, and scalability of the proposed CCPSO-R. Comparison with a representative dynamic path planning based algorithm Multi-Agent Real-Time Pursuit (MAPS) further shows the effectiveness of CCPSO-R. Finally, the codes of this paper are public available at: https://github.com/LijunSun90/pursuitCCPSOR.
Hate Speech Detection for Indonesia Tweets Using Word Embedding And Gated Recurrent Unit

Social media has changed the people mindset to express thoughts and moods. As the activity of social media users increases, it does not rule out the possibility of crimes of spreading hate speech can spread quickly and widely. So that it is not possible to detect hate speech manually. GRU is one of the deep learning methods that has the ability to learn information relations from the previous time to the present time. In this research feature extraction used is word2vec, because it has the ability to learn semantics between words. In this research the GRU performance will be compared with other supervision methods such as support vector machine, naive bayes, decision tree and logistic regression. The results obtained show that the best accuracy is 92.96% by the GRU model with word2vec feature extraction. The use of word2vec in the comparison supervision method is not good enough from tf and tf-idf.
Understanding the credential theft lifecycle

Credential theft is a serious driver of cybercrime today. The world over, different kinds of credentials are used by billions daily to authenticate themselves in their physical and digital lives. From physical keys, to tokens and cards, to digital private keys, session cookies, digital certificates, crypto-currency wallets, login and password combinations, all of these types of credentials are vulnerable to attack. Credential theft is a serious driver of cybercrime today. The world over, different kinds of credentials are used by billions daily to authenticate themselves in their physical and digital lives. From physical keys, through tokens and cards, to digital private keys, session cookies, digital certificates, crypto-currency wallets, login and password combinations, all of these types of credentials are vulnerable to attack. But what happens after they are compromised? Jose Miguel Esparza of Blueliv follows the lifespan of stolen credentials, from theft to being exploited for profit.
Filtering free choice

Sentences involving disjunctions under a possibility modal give rise to so-called ‘free choice’ inferences, i.e. inferences to the effect that each disjunct is possible. This note investigates the interaction between free choice and presuppositions. We focus on sentences embedding both a disjunction in the scope of a possibility modal and a presupposition trigger, and we investigate how the free choice inference triggered by the former can contribute to filtering the presupposition of the latter. We consider three cases: conditionals, disjunctions and unless sentences. We observe that in all of these cases the presuppositions triggered from the consequent, second disjunct, or the scope of unless appear to be filtered by a free choice inference associated with the rest of the sentence. The case of the conditional can be accommodated by scalar accounts of free choice, but the disjunction and unless cases cause a substantial problem for all these accounts. After discarding a natural but unsuccessful attempt at a solution, we consider two more promising strategies. The first holds on to an implicature account of free choice and exploits an algorithm of free insertion of redundant material. The second is based on a semantic account of free choice based on a notion of homogeneity. Each of these solutions comes with related problems. We conclude that the correct form of a theory of free choice remains open, though the data concerning the interaction between free choice and presupposition can significantly help sharpen our theoretical choices. EARLY ACCESS
Automatic Speech Recognition System with Output-Gate Projected Gated Recurrent Unit

The long short-term memory recurrent neural network (LSTM) has achieved tremendous success for automatic speech recognition (ASR). However, the complicated gating mechanism of LSTM introduces a massive computational cost and limits the application of LSTM in some scenarios. In this paper, we describe our work on accelerating the decoding speed and improving the decoding accuracy. First, we propose an architecture, which is called Projected Gated Recurrent Unit (PGRU), for ASR tasks, and show that the PGRU can consistently outperform the standard GRU. Second, to improve the PGRU generalization, particularly on large-scale ASR tasks, we propose the Output-gate PGRU (OPGRU). In addition, the time delay neural network (TDNN) and normalization methods are found beneficial for OPGRU. In this paper, we apply the OPGRU for both the acoustic model and recurrent neural network language model (RNN-LM). Finally, we evaluate the PGRU on the total Eval2000 / RT03 test sets, and the proposed OPGRU single ASR system achieves 0.9% / 0.9% absolute (8.2% / 8.6% relative) reduction in word error rate (WER) compared to our previous best LSTM single ASR system. Furthermore, the OPGRU ASR system achieves significant speed-up on both acoustic model and language model rescoring.
An approximation to solve regression problems with a genetic fuzzy rule ordinal algorithm

Regression problems try estimating a continuous variable from a number of characteristics or predictors. Several proposals have been made for regression models based on the use of fuzzy rules; however, all these proposals make use of rule models in which the irrelevance of the input variables in relation to the variable to be approximated is not taken into account. Regression problems share with the ordinal classification the existence of an explicit relationship of order between the values of the variable to be predicted. In a recent paper, the authors have proposed an ordinal classification algorithm that takes into account the detection of the irrelevance of input variables. This algorithm extracts a set of fuzzy rules from an example set, using as the basic model a sequential covering strategy along with a genetic algorithm. In this paper, a proposal for a regression algorithm based on this ordinal classification algorithm is presented. The proposed model can be interpreted as a multiclassifier and multilevel system that learns at each stage using the knowledge gained in previous stages. Due to similarities between regression and ordinal problems as well as the use of a set of ordinal algorithms, an error interval can be returned with the regression output value. Experimental results show the good behavior of the proposal as well as the results of the error interval.
Conversational Networks for Automatic Online Moderation

Moderation of user-generated content in an online community is a challenge that has great socio-economic ramifications. However, the costs incurred by delegating this paper to human agents are high. For this reason, an automatic system able to detect abuse in user-generated content is of great interest. There are a number of ways to tackle this problem, but the most commonly seen in practice are word filtering or regular expression matching. The main limitations are their vulnerability to intentional obfuscation on the part of the users, and their context-insensitive nature. Moreover, they are language dependent and may require appropriate corpora for training. In this paper, we propose a system for automatic abuse detection that completely disregards message content. We first extract a conversational network from raw chat logs and characterize it through topological measures. We then use these as features to train a classifier on our abuse detection task. We thoroughly assess our system on a dataset of user comments originating from a French massively multiplayer online game. We identify the most appropriate network extraction parameters and discuss the discriminative power of our features, relatively to their topological and temporal nature. Our method reaches an F -measure of 83.89 when using the full feature set, improving on existing approaches. With a selection of the most discriminative features, we dramatically cut computing time while retaining the most of the performance (82.65).
Entity Personalized Talent Search Models with Tree Interaction Features

Talent Search systems aim to recommend potential candidates who are a good match to the hiring needs of a recruiter expressed in terms of the recruiter's search query or job posting. Past work in this domain has focused on linear and nonlinear models which lack preference personalization in the user-level due to being trained only with globally collected recruiter activity data. In this paper, we propose an entity-personalized Talent Search model which utilizes a combination of generalized linear mixed (GLMix) models and gradient boosted decision tree (GBDT) models, and provides personalized talent recommendations using nonlinear tree interaction features generated by the GBDT. We also present the offline and online system architecture for the productionization of this hybrid model approach in our Talent Search systems. Finally, we provide offline and online experiment results benchmarking our entity-personalized model with tree interaction features, which demonstrate significant improvements in our precision metrics compared to globally trained non-personalized models.
High-Accuracy Image Rotation and Scale Estimation Using Radon Transform and Sub-Pixel Shift Estimation

Rotation and scale estimation of images are fundamental tasks in image registration.The conventional estimation method uses log-polar transform and 1D shift estimation to estimate rotation and scale regardless of the shift of images.However, this transform requires interpolation of the frequency components, which causes estimation error.We propose a rotation and scale estimation algorithm based on the Radon transform and sub-pixel shift estimation.The Radon transform can estimate the rotation independent of the shift and can reduce the influence of interpolation error.In addition, sub-pixel shift estimation using a linear approximation of the phase component improves the precision of 1D shift estimation and achieves accurate rotation estimation.The proposed method was evaluated on test images, and the results demonstrate that the proposed method has higher accuracy compared with the log-polar transform.
A collaborative secret sharing scheme based on the Chinese Remainder Theorem

Secret sharing (SS) can be used as an important group key management technique for distributed cloud storage and cloud computing. In a traditional threshold SS scheme, a secret is shared among a number of participants and each participant receives one share. In many real-world applications, some participants are involved in multiple SS schemes with group collaboration supports thus have more privileges than the others. To address this issue, we could assign multiple shares to such participants. However, this is not a bandwidth efficient solution. Therefore, a more sophisticated mechanism is required. In this paper, we propose an efficient collaborative secret sharing (CSS) scheme specially tailored for multi-privilege participants in group collaboration. The CSS scheme between two or among more SS schemes is constructed by rearranging multi-privilege participants in each participant set and then formulated into several independent SS schemes with multi-privilege shares that precludes information leakage. Our scheme is based on the Chinese Remainder Theorem with lower recovery complexity and it allows each multi-privilege participant to keep only one share. It can be formally proved that our scheme achieves asymptotically perfect security. The experimental results demonstrate that it is efficient to achieve group collaboration, and it has computational advantages, compared with the existing works in the literature.
Integrating ICT in Education: An Adaptive Learning System Based on Users’ Context in Mobile Environments

The integration of Information and Communication Technologies in the educational area makes courses at universities more accessible anytime and anywhere. However, learners have different background knowledge, learning styles and they learn in different contexts and situations. For this purpose, we suggest in this paper a new model of adaptive mobile learning systems that considers a combination of learner’s characteristics and context in order to provide each learner with the most suitable learning content and format of presentation.
Space-Efficient Fully Dynamic DFS in Undirected Graphs †

Depth-first search (DFS) is a well-known graph traversal algorithm and can be performed in O ( n + m ) time for a graph with n vertices and m edges. We consider the dynamic DFS problem, that is, to maintain a DFS tree of an undirected graph G under the condition that edges and vertices are gradually inserted into or deleted from G. We present an algorithm for this problem, which takes worst-case O ( m n · polylog ( n ) ) time per update and requires only ( 3 m + o ( m ) ) log n bits of space. This algorithm reduces the space usage of dynamic DFS algorithm to only 1.5 times as much space as that of the adjacency list of the graph. We also show applications of our dynamic DFS algorithm to dynamic connectivity, biconnectivity, and 2-edge-connectivity problems under vertex insertions and deletions.
Towards Integration of Cognitive Models in Dialogue Management: Designing the Virtual Negotiation Coach Application

This paper presents an approach to flexible and adaptive dialogue management driven by cognitive modelling of human dialogue behaviour. Artificial intelligent agents, based on the ACT-R cognitive architecture, together with human actors are participating in a (meta)cognitive skills training within a negotiation scenario. The agent employs instance-based learning to decide about its own actions and to reflect on the behaviour of the opponent. We show that task-related actions can be handled by a cognitive agent who is a plausible dialogue partner. Separating task-related and dialogue control actions enables the application of sophisticated models along with a flexible architecture in which various alternative modelling methods can be combined. We evaluated the proposed approach with users assessing the relative contribution of various factors to the overall usability of a dialogue system. Subjective perception of effectiveness, efficiency and satisfaction were correlated with various objective performance metrics, e.g. number of (in)appropriate system responses, recovery strategies, and interaction pace. It was observed that the dialogue system usability is determined most by the quality of agreements reached in terms of estimated Pareto optimality, by the user's negotiation strategies selected, and by the quality of system recognition, interpretation and responses. We compared human-human and human-agent performance with respect to the number and quality of agreements reached, estimated cooperativeness level, and frequency of accepted negative outcomes. Evaluation experiments showed promising, consistently positive results throughout the range of the relevant scales.
Augmented Reality: Poised for Impact

Though augmented reality (AR) has existed for decades, the technology's full impact is just beginning to become apparent. Since its inception, AR has shown significant promise for use in a wide variety of fields, including education, entertainment, art, and the sciences. This special issue focuses specifically on the impact of this unique technology on healthcare, particularly the areas of clinical training, patient education and treatment, and its use as a robust assessment tool. Now that technology is catching up with imagination, researchers are able to investigate these applications in earnest.
Adaptive projection augmented reality with object recognition based on deep learning

This study describes an Adaptive Projection Augmented Reality (AR) system that can provide information in real-time using object recognition. This approach is based on deep-learning through the construction of a 3D space from the real world. Through single system, a projector-camera unit with pan-tilt mechanism,, the 360-degree space of user surroundings can be built into a 3-dimensional space and accomplish object and user recognition. a projection AR environment can be generated instantaneously. Information relevant to real-world objects can be provided through real-time interactions between the user and objects. Using spatial interaction, it also allows for achieving intuitive interactions with projection information, user interface (UI), and contents without touch sensors. Several scenarios for the use of this system are described below.
A Neural Network Approximation Based on a Parametric Sigmoidal Function

It is well known that feed-forward neural networks can be used for approximation to functions based on an appropriate activation function. In this paper, employing a new sigmoidal function with a parameter for an activation function, we consider a constructive feed-forward neural network approximation on a closed interval. The developed approximation method takes a simple form of a superposition of the parametric sigmoidal function. It is shown that the proposed method is very effective in approximation of discontinuous functions as well as continuous ones. For some examples, the availability of the presented method is demonstrated by comparing its numerical results with those of an existing neural network approximation method. Furthermore, the efficiency of the method in extended application to the multivariate function is also illustrated.
Truly Proximal Policy Optimization

Proximal policy optimization (PPO) is one of the most successful deep reinforcement-learning methods, achieving state-of-the-art performance across a wide range of challenging tasks. However, its optimization behavior is still far from being fully understood. In this paper, we show that PPO could neither strictly restrict the likelihood ratio as it attempts to do nor enforce a well-defined trust region constraint, which means that it may still suffer from the risk of performance instability. To address this issue, we present an enhanced PPO method, named Truly PPO. Two critical improvements are made in our method: 1) it adopts a new clipping function to support a rollback behavior to restrict the difference between the new policy and the old one; 2) the triggering condition for clipping is replaced with a trust region-based one, such that optimizing the resulted surrogate objective function provides guaranteed monotonic improvement of the ultimate policy performance. It seems, by adhering more truly to making the algorithm proximal - confining the policy within the trust region, the new algorithm improves the original PPO on both sample efficiency and performance.