Reviewerly/exHarmony huggingface dataset:
Traffic Light Recognition and Ranging System Based on Machine Vision

The process of traffic information has become an important aspect of automated driving nowadays. Thus, in this paper a small mobile system is proposed, in which MobileNetV2+SSD model is deployed and combined with binocular stereo-vision ranging to realize the recognition and ranging of traffic lights. Experiment results with real images show that this system can effectively process the information of traffic lights under real road conditions.
Multi-party PSM, Revisited:

We improve the communication complexity in the Private Simultaneous Messages (PSM) model, which is a minimal model of non-interactive information-theoretic multi-party computation. The state-of-the-art PSM protocols were recently constructed by Beimel, Kushilevitz and Nissim (EUROCRYPT 2018).We present new constructions of k-party PSM protocols. The new protocols match the previous upper bounds when \(k=2\) or 3 and improve the upper bounds for larger k. We also construct 2-party PSM protocols with unbalanced communication complexity. More concretely, For infinitely many k (including all \(k \le 20\)), we construct k-party PSM protocols for arbitrary functionality \(f:[N]^k\rightarrow \{0,1\}\), whose communication complexity is \(O_k(N^{\frac{k-1}{2}})\). This improves the former best known upper bounds of \(O_k(N^{\frac{k}{2}})\) for \(k\ge 6\), \(O(N^{7/3})\) for \(k=5\), and \(O(N^{5/3})\) for \(k=4\). For all rational \(0<\eta <1\) whose denominator is \(\le 20\), we construct 2-party PSM protocols for arbitrary functionality \(f:[N]\times [N]\rightarrow \{0,1\}\), whose communication complexity is \(O(N^\eta )\) for one party, \(O(N^{1-\eta })\) for the other. Previously the only known unbalanced 2-party PSM has communication complexity \(O(\log (N)), O(N)\).
Mask Based Unsupervised Content Transfer

We consider the problem of translating, in an unsupervised manner, between two domains where one contains some additional information compared to the other. The proposed method disentangles the common and separate parts of these domains and, through the generation of a mask, focuses the attention of the underlying network to the desired augmentation alone, without wastefully reconstructing the entire target. This enables state-of-the-art quality and variety of content translation, as demonstrated through extensive quantitative and qualitative evaluation. Our method is also capable of adding the separate content of different guide images and domains as well as remove existing separate content. Furthermore, our method enables weakly-supervised semantic segmentation of the separate part of each domain, where only class labels are provided. Our code is publicly available at this https URL.
Handwritten Character Recognition by using Convolutional Deep Neural Network; Review

Handwritten character recognition is an important domain of research with implementation in varied fields.  Past and recent works in this field focus on diverse languages to utilize the character recognition in automated data-entry applications. Deep Neural network studies recognize the individual characters in the form images. The reliance of each recognition, which is provided by the neural network as part of the ranking result, is one of the things used to customize the implementation to the request of the client. Convolutional Deep neural network model is reviewed to recognize the handwritten characters in this study. This model, initially, learned a useful set of support by using core and local receptive areas and then a densely connected network layers are employed for the discernment task.
Machine Learning-Based Predictions of Customers’ Decisions in Car Insurance

Predicting customer decisions allows companies to obtain higher profits due to better resource management. The accuracy of those predictions can be currently boosted by the application of machine learning algorithms.We propose a new method to predict a car driver’s decision about taking a replacement car after a vehicle accident happens. We use feature engineering to create attributes of high significance. The generated attributes are related to time (e.g., school holidays), place of collision (e.g., distance from home), time and conditions (e.g., weather), vehicles (e.g., vehicle value), addresses of both the victim and the perpetrator. Feature engineering involves external sources of data.Five machine learning methods of classification are considered: decision trees, multi-layer perceptrons, AdaBoost, logistic regression and gradient boosting. Algorithms are tested on real data from a Polish insurance company. Over 80% accuracy of prediction is achieved. Significance of the attributes is calculated using the linear vector quantization method.Presented work shows the applicability of machine learning in the car insurance market.
LumièreNet: Lecture Video Synthesis from Audio

We present LumiereNet, a simple, modular, and completely deep-learning based architecture that synthesizes, high quality, full-pose headshot lecture videos from instructor's new audio narration of any length. Unlike prior works, LumiereNet is entirely composed of trainable neural network modules to learn mapping functions from the audio to video through (intermediate) estimated pose-based compact and abstract latent codes. Our video demos are available at [22] and [23].
A Dynamic Programming Track-Before-Detect Algorithm with Adaptive State Transition Set

Due to the use of a fixed-size state transition set, the traditional dynamic programming Track-Before-Detect (DP-TBD) algorithm significantly reduces the detection and tracking performance of maneuvering targets. This paper proposes a DP-TBD algorithm with an adaptive state transition set (ASTS-DP-TBD). The algorithm improves the search efficiency of the maneuvering target by introducing Kalman filtering and target state transition probability in the traditional algorithm. In addition, this paper also optimizes the termination decision strategy of the algorithm, which significantly improves the detection performance. Simulation results show that the proposed algorithm in this paper has better detection and tracking results than traditional algorithms for maneuvering targets.
HNS Ontology Using Faceted Approach

The purpose of this research is to develop an ontology with subsequent testing and evaluation, for identifying utility and value. The domain that has been chosen is human nervous system (HNS) disorders. It is hypothesized here that an ontology-based patient records management system is more effective in meeting and addressing complex information needs of health-care personnel. Therefore, this study has been based on the premise that developing an ontology and using it as a component of the search interface in hospital records management systems will lead to more efficient and effective management of health-care. It is proposed here to develop an ontology of the domain of HNS disorders using a standard vocabulary such as MeSH or SNOMED CT. The principal classes of an ontology include facet analysis for arranging concepts based on their common characteristics to build mutually exclusive classes. We combine faceted theory with description logic, which helps us to better query and retrieve data by implementing an ontological model. Protégé 5.2.0 was used as ontology editor. The use of ontologies for domain modelling will be of acute help to doctors for searching patient records. In this paper we show how the faceted approach helps us to build a flexible model and retrieve better information. We use the medical domain as a case study to show examples and implementation.
Capacity Bounded Differential Privacy

Differential privacy, a notion of algorithmic stability, is a gold standard for measuring the additional risk an algorithm's output poses to the privacy of a single record in the dataset. Differential privacy is defined as the distance between the output distribution of an algorithm on neighboring datasets that differ in one entry. In this work, we present a novel relaxation of differential privacy, capacity bounded differential privacy, where the adversary that distinguishes output distributions is assumed to be capacity-bounded -- i.e. bounded not in computational power, but in terms of the function class from which their attack algorithm is drawn. We model adversaries in terms of restricted f-divergences between probability distributions, and study properties of the definition and algorithms that satisfy them.
An Intelligent Platform with Automatic Assessment and Engagement Features for Active Online Discussions

In a university context, discussion forums are mostly available in Learning and Management Systems (LMS) but are often ineffective in encouraging participation due to poorly designed user interface and the lack of motivating factors to participate. Our integrated platform with the Telegram mobile app and a web-based forum, is capable of automatic thoughtfulness assessment of questions and answers posted, using text mining and Natural Language Processing (NLP) methodologies. We trained and applied the Random Forest algorithm to provide instant thoughtfulness score prediction for the new posts contributed by the students, and prompted the students to improve on their posts, thereby invoking deeper thinking resulting in better quality contributions. In addition, the platform is designed with six features to ensure that students remain actively engaged on the platform. We report the performance of our platform based on our implementations for a university course in two runs, and compare with existing systems to show that by using our platform, students’ participation and engagement are highly improved, and the quality of posts will increase. Most importantly, our students’ performance in the course was shown to be positively correlated with their participation in the system.
Efficient FPGA Emulation of Quantum Fourier Transform

Quantum computation shows its great advantage on exponential speed-up for some complex algorithms. Compared to software simulation with sequential execution, hardware emulation based on Field Programmable Gate Array (FPGA) can help to realize the intrinsic quantum parallelism. Quantum Fourier Transform (QFT) is the core of several quantum algorithms like Shor's algorithm. This paper proposes a recursive decomposition of QFT-n, which can be emulated with a pipeline architecture based on Intel Stratix IV FPGA device. Although QFT-8 is much more complex than QFT-5, experimental results show that our QFT-8 architecture can reduces 91% DSP blocks and 76% combinational ALUTs at the cost of 11x registers compared with QFT-5 in [4] on average.
Querying Knowledge via Multi-Hop English Questions

Abstract The inherent difficulty of knowledge specification and the lack of trained specialists are some of the key obstacles on the way to making intelligent systems based on the knowledge representation and reasoning (KRR) paradigm commonplace. Knowledge and query authoring using natural language, especially controlled natural language (CNL), is one of the promising approaches that could enable domain experts, who are not trained logicians, to both create formal knowledge and query it. In previous work, we introduced the KALM system (Knowledge Authoring Logic Machine) that supports knowledge authoring (and simple querying) with very high accuracy that at present is unachievable via machine learning approaches. The present paper expands on the question answering aspect of KALM and introduces KALM-QA (KALM for Question Answering) that is capable of answering much more complex English questions. We show that KALM-QA achieves 100% accuracy on an extensive suite of movie-related questions, called MetaQA , which contains almost 29,000 test questions and over 260,000 training questions. We contrast this with a published machine learning approach, which falls far short of this high mark.
Developing a secured image file management system using modified AES

Images are a means to share and convey relevant data in today’s digital world. This paper presents an image file management system to provide a platform for distributing and viewing images in a secured manner. The shared image files are stored in the server in an encrypted manner to provide additional security to the owner of the file. A modified AES algorithm using bit permutation was used to encrypt the image files. Based on the experimental result, image files were successfully encrypted in the server and can only be decrypted by the intended recipient of the file providing an efficient and reliable way of exchanging images.
Review of Research on Task-Oriented Spoken Language Understanding

Abstract Spoken language understanding(SLU) is an important function module of the dialogue system. Slot filling and intent detection are two key sub-tasks of task-oriented spoken language understanding. In recent years, the methods of joint recognition have become the mainstream methods of spoken language understanding to solve slot filling and intent detection. Since deep neural network has advantages such as strong generalization and autonomous learning characteristics compared with traditional methods. So far, slot filling and intent detection have been developed from traditional methods to deep neural network methods, and the performance has also been significantly improved. This paper introduces the methods of two tasks from the independent model to the joint model. It focuses on the joint modeling methods based on deep neural network, analyzes current problems and future development trend of two sub-tasks.
Comparing Random Forest and Gaussian Process Modeling in the Gp-demo Algorithm

In surrogate-model-based optimization, the selection of an appropriate surrogate model is very important.If solution approximations returned by a surrogate model are accurate and with narrow confidence intervals, an algorithm using this surrogate model needs less exact solution evaluations to obtain results comparable to an algorithm without surrogate models.In this paper we compare two well known modeling techniques, random forest (RF) and Gaussian process (GP) modeling.The comparison includes the approximation accuracy and confidence in the approximations (expressed as the confidence interval width).The results show that GP outperforms RF and that it is more suitable for use in a surrogatemodel-based multiobjective evolutionary algorithm.
Emergence of Belief Systems and the Future of Artificial Intelligence

In the Meaningful-Based Cognitive Architecture (MBCA), every evaluation cycle the input sensory vector is propagated through a hierarchy of Hopfield-like Network (HLN) functional groups, and subsymbolic processing of the input vector occurs. The processed sensory input vector is also propagated to causal groups of HLNs and/or logic/working memory groups of HLNs, where it is matched against the vector from the MBCA’s intuitive and learned logic, physics, psychology and goal planning—collectively forming its world model or belief system. The output of the logic/working memory can be propagated back to the start of the subsymbolic system where it can be the input for the next evaluation cycle, and allows multiple symbolic processing steps to occur on intermediate results. In this fashion, full symbolic causal processing of the input sensory vector occurs. In order to achieve causal and symbolic processing of sensory input, i.e., allowing the MBCA to not only recognize but interpret events in the environment and produce motor outputs in anticipation of future outcomes, every evaluation cycle the MBCA must not simply process the input sensory vector for subsymbolic recognition, but must process this vector in terms of the belief system activated. Artificial Neural Networks have excellent pattern recognition and reinforcement learning abilities, but perform poorly at causally and logically making sense of their environment. Future artificial intelligence systems that attempt to overcome the limitations of subsymbolic architectures by the integration of symbolic processing, may be at risk for developing faulty belief systems with unintended results.
Asymptotic Reversibility of Thermal Operations for Interacting Quantum Spin Systems via Generalized Quantum Stein's Lemma

For quantum spin systems in any spatial dimension with a local, translation-invariant Hamiltonian, we prove that asymptotic state convertibility from a quantum state to another one by a thermodynamically feasible class of quantum dynamics, called thermal operations, is completely characterized by the Kullback-Leibler (KL) divergence rate, if the state is translation-invariant and spatially ergodic. Our proof consists of two parts and is phrased in terms of a branch of the quantum information theory called the resource theory. First, we prove that any states, for which the min and max Renyi divergences collapse approximately to a single value, can be approximately reversibly converted into one another by thermal operations with the aid of a small source of quantum coherence. Second, we prove that these divergences collapse asymptotically to the KL divergence rate for any translation-invariant ergodic state. We show this via a generalization of the quantum Stein's lemma for quantum hypothesis testing beyond independent and identically distributed (i.i.d.) situations. Our result implies that the KL divergence rate serves as a thermodynamic potential that provides a complete characterization of thermodynamic convertibility of ergodic states of quantum many-body systems in the thermodynamic limit, including out-of-equilibrium and fully quantum situations.
Noisy Interactive Quantum Communication

We study the problem of simulating protocols in a quantum communication setting over noisy channels. This problem falls at the intersection of quantum information theory and quantum communication complexity, and it will be of importance for eventual real-world applications of interactive quantum protocols, which can be proved to have exponentially lower communication costs than their classical counterparts for some problems. These are the first results concerning the quantum version of this problem, originally studied by Schulman in a classical setting [L. J. Schulman, Communication on noisy channels: A coding theorem for computation, in Proceedings of the 33rd Annual IEEE Symposium on Foundations of Computer Science, IEEE, 1992, pp. 724--733], [L. J. Schulman, Deterministic coding for interactive communication, in Proceedings of the 25th Annual ACM Symposium on Theory of Computing, ACM, 1993, pp. 747--756]. We simulate a length $N$ quantum communication protocol by a length $O(N)$ protocol with arbitrarily small error. Under adversarial noise, our strategy can withstand, for arbitrarily small $\varepsilon>0$, error rates as high as $1/2-\varepsilon$ when parties preshare perfect entanglement, but the classical channel is noisy. We show that this is optimal. We provide extension of these results in several other models of communication, including when also the entanglement is noisy, and when there is no preshared entanglement but communication is quantum and noisy. We also study the case of random noise, for which we provide simulation protocols with positive communication rates and no preshared entanglement over some quantum channels with quantum capacity $C_Q=0$, proving that $C_Q$ is in general not the right characterization of a channel's capacity for interactive quantum communication. Our results are stated for a general quantum communication protocol in which Alice and Bob collaborate, and these results hold in particular in the quantum communication complexity settings of the Yao and Cleve--Buhrman models.
Machine Learning in Microbiology: Finding the Signal in the Noise

Electronic health records (EHRs) capture data that is common to healthcare organizations to document the care they provide to their patients. EHR data are discrete or structured data that include laboratory values and diagnosis codes. Other examples include unstructured data like physician notes, nursing notes, and dictations. As access to EHR data expands, significant opportunities arise for clinical laboratories to combine EHR data with data internal to the laboratory to answer questions about laboratory impact. For example, microbiology laboratories can collect clinical, financial and operational data variables to document downstream improvements to survival, stewardship, test utilization after the implementation of a diagnostic intervention or a clinical decision tree that requires results of a laboratory test [1]. As clinical decision trees are adopted, data can be used to risk-stratify patients and to guide care for patients with high-complexity conditions and develop diagnostic or clinical pathways that improve care. For new diagnostic pathways to occur, the applications of computational techniques, such as machine learning (ML) fit well with data mining the EHR. ML is a branch of artificial intelligence and is defined as an analytic model that can handle large complex data sets and “learn” from the data at hand to identify and predict patterns with minimal human intervention. Application of ML has the potential to transform patient risk stratification, which could then transform care. In this review, the basics of ML and examples of relevant ML applications are described. Finally, relevant factors for the design and implementation of ML in microbiology are discussed.
Noisy combinatorial optimisation by evolutionary algorithms

We investigate the effectiveness of a set of evolutionary algorithms on noisy combinatorial optimisation problems. Despite some of these having polynomial runtime bounds for noisy OneMax, we find that in practice they are not able to solve this problem in reasonable time, with the exception of the Paired Crossover EA, and UMDA. We further study the performance of these two algorithms on noisy versions of SubsetSum and Knapsack.
Quantum radars expose stealth planes

Stealth planes currently go under the radar, figuratively speaking, but maybe the higher definition promised by quantum radars could be used to expose them. Radar technology has become an ever more important tool in warfare since the Second World War, used to monitor the electromagnetic spectrum to detect and track enemy aircraft, missiles, satellites and other systems that in turn have grown more sophisticated in evading detection. Building better, more sensitive and harder-to-detect radar systems remains a key focus for defence manufacturers today, especially since in October 2018 the US Navy designated the electromagnetic spectrum as a warfighting domain on par with sea, land, air, space and cyber. Last year, China's biggest defence electronics company, state-owned China Electronics Technology Group, announced it had developed a next-generation quantum radar system' that, it claimed, can detect ballistic missiles and other objects flying at high speed through space. Two years previously, the group said it had tested a quantum radar to a range of 100km (60 miles). The expectation is that quantum radar, due to the unique behaviour of quantum entanglement, could, among other things, 'un-stealth' aircraft by detecting objects with a greater level of accuracy than conventional radar. Therefore, if China's announcement is true - and it hasn't provided any evidence to back its claim - it would be a huge win for both its defence and quantum capabilities.
Forgetting Memories and Their Attractiveness

We study numerically the memory that forgets, introduced in 1986 by Parisi by bounding the synaptic strength, with a mechanism that avoids confusion; allows remembering the pattern learned more recently; and has a physiologically very well-defined meaning. We analyze a number of features of this learning for a finite number of neurons and finite number of patterns. We discuss how the system behaves in the large but finite [Formula: see text] limit. We analyze the basin of attraction of the patterns that have been learned, and we show that it is exponentially small in the age of the pattern.