Reviewerly/exHarmony huggingface dataset:
Adiabatic Quantum Computing

In this chapter, we discuss the notion of a quantum simulator as a device that emulates a complex quantum many-body Hamiltonian, and a quantum annealer as an extension of such a paradigm that focuses on the preparation of the ground state in those Hamiltonians. Starting from the Landau–Zener processes and the adiabatic theorem, we illustrate how such ground states can be prepared by slow (adiabatic) deformations of a Hamiltonian. We discuss how this results in an adiabatic quantum computing algorithm and how, depending on the Hamiltonian we apply it to, we can solve problems of different classical and quantum complexity. The chapter closes with a thorough discussion of the D-Wave quantum annealer as a real-world superconducting quantum simulator of Ising-type models. This discussion centers both on the design of the superconducting annealers as well as on the conclusion from the literature on how this device works in practice, including how quantum operations are still possible even in the pressence of decoherence. We close the chapter with an outlook on the challenges that need to be overcome for making coherent quantum annealers and universal adiabatic quantum computers.
JADETraderPal – JADE mobile agent embedded with flexible negotiation strategies for dynamic e-commerce environments

Automated negotiation on qualitative issues taking linguistic values poses the challenge of computation of their offer values while generating proposals/counter-proposals. In order to make the offer values computable for the qualitative issues just as quantitative issues, the proposed flexible negotiation strategies (FlexiNegoS+) for e-commerce negotiations, generate a triangular fuzzy number corresponding to each linguistic value of the qualitative issue from which numerical reservation values are identified. JADE based mobile agent named as ‘JADETraderPal’ has been designed and embedded with FlexiNegoS+ which can act on behalf of its owner in bilateral/multilateral single-issue/multi-issue dynamic e-commerce negotiation environments. The negotiation model of FlexiNegoS+ enables JADETraderPal to make adjustable rate of concession during each proposal/counter-proposal depending upon the dynamicity of the e-market. Performance of JADETraderPal and effectiveness of FlexiNegoS+ are evaluated by developing a prototype multi-agent system representing bilateral/multilateral negotiation scenarios with parameters such as turnaround time, deal success rate and traders’ utility. The empirical results prove that JADETraderPal outperforms static agents, improves the rate of reaching consensus and maximizes the utility of its owner. JADETraderPal demonstrated robust performance even in multi-lateral negotiations and is able to increase negotiation success rates and traders’ utility in multi-lateral negotiation scenarios than in bilateral negotiation environments.
Automatic short answer grading using rough concept clusters

Evaluation of text-based answers has stayed as a challenge for researchers in recent years and with the growing acceptance of e-learning system, a solution needs to be achieved fast. While assessing the knowledge content, correctness of expression and linguistic patterns are complex issues in themselves, a smaller answer may be evaluated using keyword matching only. The work proposed in this paper is aimed at evaluating smaller text answers, no longer than a single sentence using keyword matching. The proposed method agglomerates keywords from a group of model answers forming clusters of words. The evaluation process thereafter exploits the inherent roughness of the keyword clusters to evaluate a learners' response through comparison and keyword matching. The novelty in the proposed system lies in the usage of fuzzy membership functions along with rough set theory to evaluate the answers. Rigorous tests have been conducted on dataset built for the purpose returned good correlation values with the average of two human evaluators. The proposed system also fares better than latent semantic analysis (LSA) based and link grammar-based evaluation systems.
A Binary-Feature-Based Object Recognition Accelerator With 22 M-Vector/s Throughput and 0.68 G-Vector/J Energy-Efficiency for Full-HD Resolution

Considering that the binary-feature-based approximate nearest neighbor (ANN) search technique has not been fully exploited to date, a multisegment binary feature-based hierarchical clustering tree model is proposed to achieve fast binary feature matching (FM). In addition, the multisegment vocabulary forest, is developed for the ease of hardware-oriented implementation. During the ANN searching process, the corresponding leaf nodes of each segment of the query feature are returned simultaneously to improve processing speed and accuracy. Furthermore, a hierarchical decomposition based on the term frequency-inverse document frequency is used to reduce the run-time search space and total memory footprint for object database storage. Finally, a fine-grained feature-level fully pipelined object recognition accelerator is implemented based on a dedicated design between FM and object scoring. The performance of the proposed object recognition accelerator is evaluated based on TSMC 65 nm CMOS technology. The accelerator achieves 22 M-vec/s and $6.8 \boldsymbol \times 10^{\mathbf {8}}$ vec/J in throughput and energy efficiency for full-HD resolution, respectively; these results represent a $10.6\boldsymbol \times $ and $9\boldsymbol \times $ improvement, respectively, relative to current state-of-the-art solutions. The average power consumption is 32.6 mW when operating at 200 MHz.
Anti-Continuous Collisions User Based Unpredictable Iterative Password Salted Hash Encryption

Recently, providing services remotely over a network to enhance functionality and usability has become a trend. Applications regularly contain sensitive and personal information that should be managed and controlled by authorised persons. Thus, keeping the password security is important to prevent unauthorised users from impersonating other legal users. Hashing the password is one of the safety methods and means of preventing attacks. In this study, we proposed an iterative salted hash encryption mechanism named anti-continuous collision salted hash encryption (ACCSHE) that uses cryptographically secure pseudo random number generator (CSPRNG) and hashed with password data by secured hashing algorithm (SHA-256). The outcome manifests a flexibility of changing the results by controlling several key features, such as salt used, the range of the hashing process iterations or the minimum number of iterations without affecting the system's usability. The system efficiently prevented continues collision issue and effective processing time cost for an attacker.
Beyond Precision: A Study on Recall of Initial Retrieval with Neural Representations

Vocabulary mismatch is a central problem in information retrieval (IR), i.e., the relevant documents may not contain the same (symbolic) terms of the query. Recently, neural representations have shown great success in capturing semantic relatedness, leading to new possibilities to alleviate the vocabulary mismatch problem in IR. However, most existing efforts in this direction have been devoted to the re-ranking stage. That is to leverage neural representations to help re-rank a set of candidate documents, which are typically obtained from an initial retrieval stage based on some symbolic index and search scheme (e.g., BM25 over the inverted index). This naturally raises a question: if the relevant documents have not been found in the initial retrieval stage due to vocabulary mismatch, there would be no chance to re-rank them to the top positions later. Therefore, in this paper, we study the problem how to employ neural representations to improve the recall of relevant documents in the initial retrieval stage. Specifically, to meet the efficiency requirement of the initial stage, we introduce a neural index for the neural representations of documents, and propose two hybrid search schemes based on both neural and symbolic indices, namely the parallel search scheme and the sequential search scheme. Our experiments show that both hybrid index and search schemes can improve the recall of the initial retrieval stage with small overhead.
FUZZY BASED REVIEW RATING PREDICTION IN E-COMMERCE

Opinion mining is an ongoing research area in e-commerce which aims at analyzing the people's opinions, sentiments and emotions. Moreover, the existing e-commerce systems allow the users to share their feedback in the form of textual reviews regarding the products and services. It also allows the consumers to give ratings for products that help in future recommendation of products. In this research work, a computational framework for efficiently predicting the consumer review ratings on the products has been proposed. The proposed framework integrates dimensionality reduction, genetic algorithm, fuzzy c-means and adaptive neuro-fuzzy inference techniques to overcome the limitations of the existing systems. Experiments have been conducted in this work using Amazon dataset consisting of reviews for different products. This system provides better performance and prediction accuracy for review ratings when it is compared with the related work.
Algorithmic justice: dispute resolution and the robot judge?

In this chapter the potential of developments in technology to transform the legal world and, in particular, for algorithms to replace judges, is addressed in a skeptical account. Taking dispute resolution - rather than more formal hearings or trials - as perhaps affording the strongest opportunity for the case to be made for technology, the chapter explores the evolution of “Alternative Dispute Resolution” (ADR) into “Online Dispute Resolution”(ODR), and reviews briefly how the component tasks of mediation might be thought to lend themselves to technological enhancement. The argument is made that technology may function as a useful tool to augment various informal dispute resolution processes but it cannot capture the essentially social nature of law or reproduce judicial reasoning in its proper sense. While accepting the ‘disruptive nature’ of new technology for many aspects of legal process, the authors cast doubt over some of the wilder claims about the future, and the feasibility and desirability of algorithmically based systems taking over decision and rulemaking powers unsupervised.
Combining Graph-Based Dependency Features with Convolutional Neural Network for Answer Triggering

Answer triggering is the task of selecting the best-suited answer for a given question from a set of candidate answers if it exists. This paper presents a hybrid deep learning model for answer triggering, which combines several dependency graph-based alignment features, namely graph edit distance, graph-based similarity, and dependency graph coverage, with dense vector embeddings from a Convolutional Neural Network (CNN). Our experiments on the WikiQA dataset show that such a combination can more accurately trigger a candidate answer compared to the previous state-of-the-art models. Comparative study on WikiQA data set shows $$5.86\%$$ absolute F-score improvement at the question level.
Fingerprint Policy Optimisation for Robust Reinforcement Learning

Policy gradient methods ignore the potential value of adjusting environment variables: unobservable state features that are randomly determined by the environment in a physical setting, but are controllable in a simulator. This can lead to slow learning, or convergence to suboptimal policies, if the environment variable has a large impact on the transition dynamics. In this paper, we present fingerprint policy optimisation (FPO), which finds a policy that is optimal in expectation across the distribution of environment variables. The central idea is to use Bayesian optimisation (BO) to actively select the distribution of the environment variable that maximises the improvement generated by each iteration of the policy gradient method. To make this BO practical, we contribute two easy-to-compute low-dimensional fingerprints of the current policy. Our experiments show that FPO can efficiently learn policies that are robust to significant rare events, which are unlikely to be observable under random sampling, but are key to learning good policies.
Sensitivity of the solar resource in solar tower plants to aerosols and water vapor

We make a sensitivity study of the solar resource in a solar tower plant, to aerosols, water vapour and solar position, considering typical atmospheric conditions observed at Ouarzazate, Morocco. Four solar resource parameters were defined: the direct normal irradiance (DNI), incident at the heliosat, the slant path attenuation between the heliostats and the receiver (Asp), the solar irradiance incident at the receiver (SIR), and the solar irradiance lost between the heliostat and the receiver (SIRloss). The aerosol optical thickness (AOT), which varies strongly from winter to summer at Ouarzazate, is the main factor on DNI and Asp. However, despite a factor of 10 in AOT between typical winter and summer conditions, Asp varies by a factor of only 2.7 and SIRloss by a factor of only 1.9, for two reasons: 1) the aerosol layer height (ALH) is correlated to AOT and has an opposite effect on Asp; 2) DNI and Asp are anti-correlated. Consequently, despite 100% standard deviation on AOT observed during 2012, the relative standard deviation of Asp is only 60%, and only 47% for SIRloss. Consequently, to estimate solar resource in a solar tower plant, it is recommended to compute directly SIR and not combining DNI and Asp derived from different sources. Also, it is recommended to make computations by considering observed variability of most atmospheric parameters at high time resolution. Computing the solar resource with the annual average of ALH instead of its seasonally dependent value generates an over estimation of Asp from 5.9% to 6.2%. With the annual average of AOT or the Ångström exponent instead of the hourly-varying values, SIR is changed by ±3%.
pretty Easy privacy (pEp): Contact and Channel Authentication through Handshake

In interpersonal messaging end-to-end encryption means for public keydistribution and verification of its authenticity are needed; thelatter to prevent man-in-the-middle (MITM) attacks. This documentproposes a new method to easily verify a public key is authentic by aHandshake process that allows users to easily authenticate theircommunication channel. The new method is targeted to OpportunisticSecurity scenarios and is already implemented in several applicationsof pretty Easy privacy (pEp).
Medication Use for Childhood Pneumonia at a Children’s Hospital in Shanghai, China: Analysis of Pattern Mining Algorithms

Background: Pattern mining utilizes multiple algorithms to explore objective and sometimes unexpected patterns in real-world data. This technique could be applied to electronic medical record data mining; however, it first requires a careful clinical assessment and validation.
Efficient Temporal Planning Using Metastates

When performing temporal planning as forward state-space search, effective state memoisation is challenging. Whereas in classical planning, two states are equal if they have the same facts and variable values, in temporal planning this is not the case: as the plans that led to the two states are subject to temporal constraints, one might be extendable into at temporally valid plan, while the other might not. In this paper, we present an approach for reducing the state space explosion that arises due to having to keep many copies of the same ‘classically’ equal state – states that are classically equal are aggregated into metastates, and these are separated lazily only in the case of temporal inconsistency. Our evaluation shows that this approach, implemented in OPTIC and compared to existing state-of-the-art memoisation techniques, improves performance across a range of temporal domains.
Stereo matching based on absolute differences for multiple objects detection

This article presents a new algorithm for object detection using stereo camera system. The problem to get an accurate object detion using stereo camera is the imprecise of matching process between two scenes with the same viewpoint. Hence, this article aims to reduce the incorrect matching pixel with four stages. This new algorithm is the combination of continuous process of matching cost computation, aggregation, optimization and filtering. The first stage is matching cost computation to acquire preliminary result using an absolute differences method. Then the second stage known as aggregation step uses a guided filter with fixed window support size. After that, the optimization stage uses winner-takes-all (WTA) approach which selects the smallest matching differences value and normalized it to the disparity level. The last stage in the framework uses a bilateral filter. It is effectively further decrease the error on the disparity map which contains information of object detection and locations. The proposed work produces low errors (i.e., 12.11% and 14.01% nonocc and all errors) based on the KITTI dataset and capable to perform much better compared with before the proposed framework and competitive with some newly available methods.
Role of intensive method of bone marrow iron assessment and serum Ferritin in prediction of iron deficiency: a study of 143 patients

Introduction:Iron deficiency anemia is a major health problem in developing countries like India.The situation is even more grim in economically backward areas of our country like Bundelkhand region.The increasing prevalence of co-existence of iron deficiency anemia with chronic diseases makes the use of biochemical markers like serum ferritin, challenging in diagnosing cases of inflammation and functional iron deficiency.Microscopic evaluation is the "gold standard" method for assessment of iron stores.In this study, we tried to correlate the marrow iron stores with serum ferritin so as to facilitate the distinction of iron store deficiency from functional iron deficiency.Materials and Methods: We selected 143 adult patients with anemia.In all the patients diagnostic bone marrow examination was done.Serum ferritin assay, CBC and peripheral blood smear examination was also performed.Assessment of bone marrow iron was done by both Gale's method and the intensive method and was correlated with serum ferritin.Results: The Conventional Gale's grading revealed hypoferremic state in 46.85% cases and normal iron stores in 53.15% cases.The new Intensive method of grading, classifies anemias according to iron status.It showed that most common was combined deficiency (functional & iron stores) (43.36%), followed by normal stores, functional iron deficiency and lastly iron stores deficiency in Bundelkhand region.We also found a positive correlation of microcytic hypochromic blood picture with S.Ferritin and iron deficiency anemia (Spearman's Rho correlation, p value=0.0001).The proposed cutoff value of S.ferritin as a surrogate marker for iron deficiency used in our study is <60ng/ml. Conclusion:The new Intensive Bone Marrow Grading Method has greatly improved iron assessment because it provides a useful iron status classification which is of high clinical importance, especially in areas with high prevalence of inflammatory conditions co-existing with iron deficiency anemia, like Bundelkhand region and remains the gold standard method.
Dreaming Mechanism for Training Bio-Inspired Driving Agents

This paper addresses one of the key ideas embraced by the European funded H2020 research project Dreams4Cars: to borrow resemblances from the way humans learn to drive. We developed an artificial driving agent, called “Co-driver”, characterized by an architecture mimicking the fundamental components of the human brain involved in the learning of complex sensorimotor abilities, like driving. We implemented a dream-like mechanism to train and test the self-driving agent, and we will show two case studies demonstrating the effectiveness of such approach.
Lane Change Prediction Using an Echo State Network

Lane change prediction can reduce accidents and increase the traffic flow. An Echo State Network is implemented for the prediction of left lane changes in an urban area. The Echo State Network has three input variables: turn signal, head rotation in y-direction and steering angle. The input variables were generated from a Naturalistic Driving study in the urban area of Chemnitz, Germany. A successful prediction for left mandatory and discretionary lane changes was realized.
Service Function Chaining: Subscriber and Performance Policy Identification Variable-Length Network Service Header (NSH) Context Headers

This document defines Subscriber and Performance Policy IdentifiersNetwork Service Header Variable-Length Context Headers to informService Functions about subscriber- and service-related informationfor the sake of policy enforcement and appropriate service functionchaining operations. The structure of each context header is defined,their use and processing instructions by SFC-aware nodes areexplained.
Applying Convolutional Neural Network for Military Object Detection on Embedded Platform

Object detection has always been an important part in the field of image processing. The traditional object detection algorithm has complex structure and operations. With the continuous development of deep learning technology, Convolutional Neural Network (CNN) has become an advanced object detection method. Because of its high accuracy, stability, and speed of operation, this method is widely used in many fields. In this work, we use CNN to achieve the detection of military objects. It uses the idea of regression to build a model, which is fast and accurate and can achieve detection in real-time. Unlike image classification, image detection requires more parameters and calculations, and therefore it is difficult to be placed on a small embedded platform. We analyzed some of state-of-the-art object detection network, replace the traditional fully connected layer with global average pool layer, generate region proposals using the anchor boxes, and apply it to military object detection. Finally, we deployed it successfully on TMS320C6678, which is a low-cost, low-power embedded platform. A well-performing and easy-to-deploy military object detection system is realized, which helps to improve the accuracy and efficiency of military operations.
Permutation-Based Signature Generation for Spread-Spectrum Video Watermarking

Generation of secure signatures suitable for spread-spectrum video watermarking is proposed. The method embeds a message, which is a two-dimensional binary pattern, into a three-dimensional volume, such as video, by addition of a signature. The message can be a mark or a logo indicating the copyright information. The signature is generated by shuffling or permuting random matrices along the third or time axis so that the message is extracted when they are accumulated after demodulation by the correct key. In this way, a message is hidden in the signature having equal probability of decoding any variation of the message, where the key is used to determine which one to extract. Security of the proposed method, stemming from the permutation, is evaluated as resistance to blind estimation of secret information. The matrix-based permutation allows the message to survive the spatial down-sampling without sacrificing the security. The downside of the proposed method is that it needs more data or frames to decode a reliable information compared to the conventional spread-spectrum modulation. However this is minimized by segmenting the matrices and applying permutation to sub-matrices independently. Message detectability is theoretically analyzed. Superiority of our method in terms of robustness to blind message estimation and down-sampling is verified experimentally.