Reviewerly/exHarmony huggingface dataset:
Revival of Quantum Interference by Modulating the Biphotons

The possibility to manipulate the wave packets of single photons or biphotons has enriched quantum optics and quantum information science, with examples ranging from faithful quantum-state mapping and high-efficiency quantum memory to the purification of single photons. Here we demonstrate another fascinating use of wave packet manipulation on restoring quantum interference. By modulating the photons' temporal wave packet, we observe the revival of postselected entanglement that would otherwise be degraded or lost due to poor quantum interference. Our study shows that the amount of the restored entanglement is only limited by the forms of modulation and can achieve full recovery if the modulation function is properly designed. Our work has potential applications in long-distance quantum communication and linear optical quantum computation, particularly for quantum repeaters and large cluster states.
Multidisciplinary Flux and Multiple Research Traditions Within Cognitive Science

Núñez et al. (2019) argue that cognitive science has failed either "to transition to a mature inter-disciplinary coherent field" (p. 782) or "to generate a successful [Lakatosian] research program" (p. 789). We argue that the former was never the intention of many early researchers within the field, while the latter is an inappropriate criterion by which to judge an entire discipline. However, we concur with Núñez et al. (2019) that the individual disciplinary balance within cognitive science has changed over time. Of particular concern is the fact that the use of computational methods appears to be on the wane.
Faster Responses Are Better Responses: Introducing Incrementality into Sociable Virtual Personal Assistants

Speech-based interactive systems, such as virtual personal assistants, inevitably use complex architectures, with a multitude of modules working in series (or less often in parallel) to perform a task (e.g., giving personalized movie recommendations via dialog). Tsai, VivianAdd modules for evoking and sustaining sociability with the user and the accumulation of processing latencies through the modules results in considerable turn-taking delays. Baumann, TimoWe introduce incremental speech processing into the generation pipeline of the system to overcome this challenge with only minimal changes to the system architecture, through partial underspecification that is resolved as necessary. Pecune, FlorianA user study with a sociable movie recommendation agent objectively diminishes turn-taking delays; furthermore, users not only rate the incremental system as more responsive, but also rate its recommendation performance as higher. Cassell, Justine
A 1-norm regularized linear programming nonparallel hyperplane support vector machine for binary classification problems

This research proposes a 1-norm regularized linear programming nonparallel hyperplane support vector machine (LNSVM) model to solve binary classification problems and enhance the robustness performance. Numerous nonparallel support vector machine (SVM) models have been studied with outstanding performance on classification tasks. However, most nonparallel SVM models require two independent models to determine hyperplanes. In addition, due to the involvement of the 2-norm terms, traditional SVM models may suffer from the lack of robustness to outliers and irrelevant features. Therefore, the LNSVM model is proposed by reformulating a typical nonparallel SVM model through the 1-norm regularization. By applying the exterior penalty theory, the proposed LNSVM model is converted to the dual exterior penalty problem, which is solved by the Newton–Armijo algorithm. The essential differences that distinguish the LNSVM model from other nonparallel SVM models are: (1) Different from typical nonparallel SVM models, which solve two quadratic programming (QP) problems, the proposed LNSVM model determines two nonparallel hyperplanes simultaneously by solving a single linear programming (LP) model; (2) The robustness performance of the proposed LNSVM model has been enhanced to tolerate noisy data through the involvement of 1-norm loss function, which can also eliminate redundant features by generating sparse solution during the training procedure. The performance of the proposed LNSVM model is tested through a comparison with state-of-art SVM-based classifiers using a synthetic dataset and 11 practical benchmark datasets. The experimental results show the superiority of the proposed LNSVM model, by achieving better classification performance regarding accuracy, sensitivity, specificity, and removing redundant features synchronously.
How to Use Undiscovered Information Inequalities: Direct Applications of the Copy Lemma

We discuss linear programming techniques that help to deduce corollaries of non-classical inequalities for Shannon's entropy. We focus on direct applications of the copy lemma. These applications involve implicitly some (known or unknown) non-classical universal inequalities for Shannon's entropy, though we do not derive these inequalities explicitly. To reduce the computational complexity of these problems, we extensively use symmetry considerations. We present two examples of use of these techniques: we provide a reduced size formal inference of the best known bound for the Ingleton score (originally proven by Dougherty et al. with explicitly derived non-Shannon type inequalities), and improve the lower bound for the optimal information ratio of the secret sharing scheme for an access structure based on the Vámos matroid.
On Minimizing Service Access Latency: Employing MEC on the Fronthaul of Heterogeneous 5G Architectures

Multiple-access Edge Computing (MEC) has been proposed as a means to minimize the user to service path latency, by deploying and operating datacenter resources close to the network edge. The introduction of 5G mobile network services, and their provisioning through disaggregated base stations complying with the Cloud-RAN paradigm, allows the redefinition of the traditional Edge Computing by offering deployment of services even closer to the network access edge. In this work, we leverage a disaggregated heterogeneous 5G infrastructure, compliant with the 5G New Radio (NR) specifications, and present a scheme for placing the services even closer to the Edge, close to the concept of fog computing. We develop a scheme for the OpenAirInterface platform that allows services to be executed close or over the machines hosting the radio services for the network access. By exploiting features for integrating heterogeneous radio resources in the cell, we are able to create a controller interface for selecting the optimal radio access technology used to serve each user of the network from the MEC service perspective. We evaluate our solution in a real testbed setup, and measure performance related indicators for our solution by using adaptive video streaming. Our results illustrate up to 80% better video qualities delivered to the end user when appropriately selecting the access technology.
Human Risk Factors in Cybersecurity

Phishing emails present a serious threat to any institution, costing individuals and companies millions of dollars every year in damages. This paper attempts to assess the human risks of a mid-sized state university by conducting an experiment in which users were phished multiple times and presented with different training types. The phishing emails contained links to a controlled server that prompted users to enter login credentials into a spoofed university login page and gathered relevant data. Our analysis shows that 44.3% of users clicked on at least one of the phishing emails, and 18.6% entered valid credentials. Additionally, we found that the majority of users (64.5%) responded to the phishing emails via mobile devices running iOS or Android and we received 98% of responses within the first twelve hours of sending the emails. Finally, our data suggests that the most effective training method to prevent users from clicking subsequent phishing emails was to provide easy-to-read documents with visual cues when users were "caught" in the act.
Features Combined Binary Descriptor Based on Voted Ring-Sampling Pattern

Most existing binary descriptors are only based on intensity and simply compare averaged intensities of one sample pair to obtain binary output. To address this issue, we propose a novel ring-sampling pattern based binary descriptor encoding both intensity and gradient. For intensity coding, a ring-sampling pattern is presented to define a number of sample points and their neighboring points given an interest point. The intensity difference between two sample points is obtained by performing the comparisons of the intensities of their corresponding neighboring points directly. Further, a majority based voting strategy is employed to obtain compact representation (1 bit or 3 bits) based on all these intensity difference among neighboring points. As for gradient coding, the gradient orientation histogram is computed for each sample point, and the gradient difference of two sample points is obtained by comparing the gradient magnitude on each orientation bin. The raw binary descriptor is constructed by concentrating the intensity and gradient differences of all the sample pairs, and two feature selection strategies are proposed to obtain the final compact descriptor, named as Features Combined Binary Descriptor based on Voted Ring-Sampling Pattern (BDVRP). The experimental results on the tasks of object recognition and image matching demonstrate the superiority and the effectiveness of the proposed descriptor, with comparison to the state-of-the-art hand-crafted binary descriptors.
The Use and Non-Use of Cybersecurity Tools Among Consumers

Cybersecurity behavior changes over time, as do the recommendations for how one may best protect themselves from cybersecurity threats. This paper examines current trends in what protective measures people take, such as using a password manager, virtual private network (VPN), or anti-malware software. The reasons why people employ these protective measures is explored, including why some choose not to and whether or not they are willing to pay for cybersecurity protective services, including on-call expert access. The evidence indicates that there is an important place for security education, training, and awareness (SETA) programs to help those that do not use such measures, as well as concerted efforts to improve the self-efficacy of individuals, especially females and adults 35 and over. About one in three individuals are willing to pay for cybersecurity services that include on-call access to cybersecurity experts. However, establishing a proper price point for such services remains critical.
OdiEnCorp: Odia–English and Odia-Only Corpus for Machine Translation

A multilingual country like India needs language corpora for low-resource languages not only to provide its citizens with technologies of natural language processing (NLP) readily available in other countries, but also to support its people in their education and cultural needs. In this work, we focus on one of the low-resource languages, Odia, and build an Odia–English parallel (OdiEnCorp) and an Odia monolingual (OdiMonoCorp) corpus. The parallel corpus is based on Odia–English parallel texts extracted from online resources and formally corrected by volunteers. We also preprocess the parallel corpus for machine translation research or training. The monolingual corpus comes from a diverse set of online resources and we organize it into a collection of segments and paragraphs, easy to handle by NLP tools. OdiEnCorp parallel corpus contains 29,346 sentence pairs and 756K English and 648K Odia tokens. OdiMonoCorp contains 2.6 million tokens in 221K sentences in 71K paragraphs. Despite their small size, OdiEnCorp and OdiMonoCorp are still the largest Odia language resources, freely available for noncommercial educational or research purposes.
On Destination Prediction Based on Markov Bridging Distributions

This letter presents an alternative, more consistent, construction for bridging distributions, which enables inferring the destination of a tracked object from the available partial sensory observations. Two algorithms are then introduced to sequentially estimate the probability of all possible endpoints within a generic Bayesian framework. They capture the influence of intended destination on the object's motion via suitably adapted stochastic models. Whilst the bridging approach has low training requirements, the proposed formulation can lead to more efficient predictors, e.g. around 65% less computations for certain models. Synthetic and real data is used to illustrate the effectiveness of the introduced algorithms.
A novel features and classifiers fusion technique for recognition of Arabic handwritten character script

Arabic is the one of the fifth spoken language across the globe (422 million speak Arabic). Accurate recognition of Arabic characters is a challenging work because of two reasons; firstly, major dissimilarities and minor similarities with other languages and secondly right to left writing style. Hence, good quality features are to be extracted and better classifiers are required to develop the optical character recognition of Arabic characters/font. This manuscript explores automatic and appropriate selection of classifiers and features sets for recognition of character in Arabic handwritten script. After analyzing the problem, this manuscript investigates a new model which is concentrated on fusion strategy. To achieve this objective, fusion of various classifiers and features has been done. The suggested work is on multi-font script recognition that contains fonts of SH Roqa, Naskh, Farsi, and Igaza. To perform experimentation, different classifiers and features which are appropriate to the script recognition for Arabic font nature have been considered and implemented. The suggested work has been executed on AHCD and IFN/ENIT datasets. The experimentation results indicate that fusion strategies bring results better than traditional methods.
Authentication of GOOSE Messages under Timing Constraints in IEC 61850 Substations

For the future generation of energy systems, secure communication is a key component in ensuring a reliable and stable operation.The actual respective standard to define the communication network architectures for substation automation is the IEC 61850.In order to address the shortcomings of IEC 61850 w.r.t.communication security, IEC 62351-6 introduces respective recommendations.However, a thorough analysis of these recommendations shows that the authenticity and integrity of time-critical protocols such as Generic Object Oriented Substation Event (GOOSE) messages are not entirely covered by the proposed security measures.Therefore, in the present contribution, implementation of the RSASSA-PSS and HMAC-SHA256 authentication are investigated for the given context.Comparison with previous works is provided and obtained results show that the HMAC scheme has a better computational time than the recommended RSASSA-PSS.Thus, adjustment of the IEC 62351-6 considering the authentication of GOOSE messages shall be considered in the next edition of the standard.
Generation of Diverse Stages in Turn-Based Role-Playing Game using Reinforcement Learning

In this study, procedural content generation (PCG) using reinforcement learning (RL) is focused. PCG is defined as the generation of game content tailored to the defined evaluation function using RL models, which is one of the examples of PCG via machine learning. Compared to other generation content areas such as computer vision and natural language process, generative models such as variational autoencoders, PixelCNN, and generative adversarial networks exhibit some difficulties for applications to the game area because during the development of a new game, the content data used for training is typically not sufficient. Hence, RL is considered to be used as a method for PCG. In particular, the stage of turn-based RPG is selected as our research target because it comprises discrete sections, and its parameters were closely related; hence, it is a challenge to generate desirable stages, and the main goal is to generate various stages guided by the designed evaluation function. Two RL models, Deep Q-Network and Deep Deterministic Policy Gradient, respectively, are selected, and the generated stages are evaluated as 0.78 and 0.85 by our designed function, respectively. By the application of the stochastic noise policy, diverse stages are successfully obtained, and those diversities are evaluated by the parameter mse and the different number of valid strategies.
Fitting ReLUs via SGD and Quantized SGD

In this paper we focus on the problem of finding the optimal weights of the shallowest of neural networks consisting of a single Rectified Linear Unit (ReLU). These functions are of the form x → max(0, 〈w, x〉) with w ∈ ℝ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> denoting the weight vector. We focus on a planted i model where the inputs are chosen i.i.d. from a Gaussian distribution and the labels are generated according to a planted weight vector. We first show that mini-batch stochastic gradient descent when suitably initialized, converges at a geometric rate to the planted model with a number of samples that is optimal up to numerical constants. Next we focus on a parallel implementation where in each iteration the mini-batch gradient is calculated in a distributed manner across multiple processors and then broadcast to a master or all other processors. To reduce the communication cost in this setting we utilize a Quanitzed Stochastic Gradient Scheme (QSGD) where the partial gradients are quantized. Perhaps unexpectedly, we show that QSGD maintains the fast convergence of SGD to a globally optimal model while significantly reducing the communication cost. We further corroborate our numerical findings via various experiments including distributed implementations over Amazon EC2.
Rinascimento: Optimising Statistical Forward Planning Agents for Playing Splendor

Game-based benchmarks have been playing an essential role in the development of Artificial Intelligence (AI) techniques. Providing diverse challenges is crucial to push research toward innovation and understanding in modern techniques. Rinascimento provides a parameterised partially-observable multiplayer card-based board game, these parameters can easily modify the rules, objectives and items in the game. We describe the framework in all its features and the game-playing challenge providing baseline game-playing AIs and analysis of their skills. We reserve to agents' hyper-parameter tuning a central role in the experiments highlighting how it can heavily influence the performance. The base-line agents contain several additional contribution to Statistical Forward Planning algorithms.
An Ensemble Learning-Based Undersampling Technique for Handling Class-Imbalance Problem

Real-world data commonly have an issue of class-imbalance, which poses a big challenge in pattern recognition and machine learning tasks. To handle this issue, we have proposed an ensemble learning-based undersampling technique using Extreme Gradient Boosting (XGBoost) and Support Vector Machine (SVM). The technique has been validated using an accident dataset obtained from a steel plant. The results explore that the proposed technique is capable of resolving the issue of class-imbalance effectively. This method outperforms traditional under-sampling technique in terms of performance metrics, i.e., geometric mean (G-mean), recall, and precision.
Observer and control design in partially observable finite Markov chains

The controllable Partially Observable Markov Decision Process (POMDP) framework has proven to be useful in different domains where one is constrained to provide incomplete information of the structure and the parameters of the problem. Sometimes, it is not easy to track and measure accurately some state variables, and it may be more effective to make decisions based on imprecise information. This paper is focused on the design of an observer (which is unknown) for a class of ergodic homogeneous finite Markov chains with partially observable states. The main goal of the proposed method is the derivation of formulas for computing an observer, and as a result, on optimal control policy. For solving the problem, we introduce a new variable, which involves the product of the policy, the observation kernel and the distribution vector. We derive the formulas to recover the variables of interest. This work considers a dynamic environment for learning the parameters of the POMDP model. The construction of the adaptive policies is based on an identification approach, where we estimate the elements of the transition matrices and utility matrices by counting the number of unobserved experiences. A numerical example is presented to illustrate the practical implications of the theoretical issues applied to a portfolio optimization problem. These findings are important and new to the literature.
A new patch selection method based on parsing and saliency detection for person re-identification

Person re-identification is an important technique towards automatic recognition of a person across non-overlapping cameras. In this paper, a novel patch selection method based on parsing and saliency detection is proposed. The algorithm is divided into two stages. The first stage, primary selection: Deep Decompositional Network (DNN) is adopted to parse a pedestrian image into semantic regions, then sliding window and color matching techniques are proposed to select pedestrian patches and remove background patches. The second stage, secondary selection: saliency detection is utilized to select reliable patches according to saliency map. Finally, PHOG, HSV and SIFT features are extracted from these patches and fused with the global feature LOMO to compensate for the inherent errors of saliency detection. By applying the proposed method on such datasets as VIPeR, PRID2011, CUHK01, CUHK03, PRID 450S and iLIDS-VID, it is found that the proposed descriptor can produce results superior to many state-of-the-art feature representation methods for person identification.
Demo Abstract: An Interoperable Avatar Framework Across Multiple Games and Blockchains

Blockchain game is the most important category of decentralized applications. However, to leverage the feature of blockchain in state-of-the-art game mechanism design is still a challenge for developers and designers. In this work, we propose an interoperable blockchain gaming framework across multiple games and chains, to facilitate next-generation blockchain games. Two demo games are implemented to validate our framework.
Automatic Basketball Detection in Sport Video Based on R-FCN and Soft-NMS

In basketball videos, the ball is always so small in the camera that its appearance feature is hard to be extracted. In this paper, we introduce a deep-learning technology to detect the basketball. Specifically, we train our basketball detection model based on the Region-based Fully Convolutional Networks (R-FCN) which uses the fully convolutional Residual Network (ResNet) as the backbone network. What's more, we apply some new techniques including Online Hard Example Mining (OHEM), Soft-NMS and multi-scale training strategy to achieve higher detection accuracy. In detail, the OHEM method can reduce the cost of fine-tuning during training by calculating the loss of the RoIs. Soft-NMS can reduce the false positive rate by decreasing the object detection score between the overlap object. And the multi-scale training can improve the detection performance by receiving the good feature from the object with different scale. Finally, we achieve a mean average precision (mAP) value of 89.7% on a public basketball dataset. It proves that applying the deep-learning approach to basketball detection is effective.