Reviewerly/exHarmony huggingface dataset:
Experimentally determining the incompatibility of two qubit measurements

Abstract We describe and realize an experimental procedure for assessing the incompatibility of two qubit measurements. The experiment consists in a state discrimination task where either measurement is used according to some partial intermediate information. The success statistics of the task provides an upper bound for the amount of incompatibility of the two measurements, as it is quantified by means of their incompatibility robustness. For a broad class of unbiased and possibly noisy qubit measurements, one can make this upper bound coincide with the true value of the robustness by suitably tuning the preparation of the experiment. We demonstrate this fact in an optical setup, where the qubit states are encoded into the photons’ polarization degrees of freedom, and incompatibility is directly accessed by virtue of a refined control on the amplitude, phase and purity of the final projection stage of the measurements. Our work thus establishes the practical feasibility of a recently proposed method for the detection of quantum incompatibility.
Intelligent Chatbot for Medical Assistance in Rural Areas

In today’s world, 918 million rural people are more easily connected to the internet than doctors. The rural part of India is still in crisis due to lack of doctors and gets only one-third of hospital facilities. 80% doctors are available only in urban areas. In this paper, an application is developed with a motive of connecting doctors and assisting medication primarily focusing on rural and urban areas. The main idea is to create a user-friendly virtual assistant which helps every citizen, mainly residing in the rural parts of India. There are few industrial bots for News, Weather, Trends and perhaps different day-after-day events that manifest themselves within the general class. The proposed application takes a prominent place in the medical field. The bot recommends the best medical practice to the patients in treating minor medical issues. In the future, this Medical Chatbot can be trained with doctors to treat some minor surgeries.
Deep Feature Rotation for Multimodal Image Style Transfer

Recently, style transfer is a research area that attracts a lot of attention, which transfers the style of an image onto a content target. Extensive research on style transfer has aimed at speeding up processing or generating high-quality stylized images. Most approaches only produce an output from a content and style image pair, while a few others use complex architectures and can only produce a certain number of outputs. In this paper, we propose a simple method for representing style features in many ways called Deep Feature Rotation (DFR), while not only producing diverse outputs but also still achieving effective stylization compared to more complex methods. Our approach is representative of the many ways of augmentation for intermediate feature embedding without consuming too much computational expense. We also analyze our method by visualizing output in different rotation weights. Our code is available at https://github.com/sonnguyen129/deep-feature-rotation.
Emergence of maximal hidden quantum correlations and its trade-off with the filtering probability in dissipative two-qubit systems

We investigate the behaviour of quantum CHSH-nonlocality, F3-steering, and usefulness for teleportation in an interacting two-qubit dissipative system. We show regimes where these three quantum correlations can be extracted by means of local filtering operations, despite them not being displayed in the bare natural time evolution. Moreover, we show the existence of local hidden state (LHS) and local hidden variable (LHV) models for some states during the dynamics and thus, showing that apparently-useless physical systems could still exhibit quantum correlations, which are hidden from us, but that can still be revealed by means of local filtering operations and therefore, displaying the phenomenon of hidden quantum correlations. Furthermore, we report on extreme versions of these phenomena, where the revealed correlations achieve the maximal amount allowed by quantum theory. This phenomenon of maximal hidden correlations relies on the qubits collective damping, and may take place even in long-distance separated qubits. Despite the immediate appeal of the physical system displaying such an extreme phenomenon, we furthermore show however, that there actually exists a trade-off between the amount of quantum correlations which can be extracted and the filtering probability with which such protocol can be implemented. Explicitly, the higher the amount of correlations to be extracted, the more difficult it becomes for the protocol to be implemented (lower filtering probability). This is consequently showing us the remarkable fact that whilst the phenomenon of maximal hidden quantum correlations does naturally emerge during the evolution of physical systems, Nature does not completely give it away for free, by imposing a limit to the rate at which this can be done. From a theoretical point of view, the existence of such trade-off imposes a fundamental limit to the extraction of quantum correlations by local filtering operations. From a practical point of view on the other hand, the results here presented determine the amount of resources that should be invested in order to extract such maximal hidden quantum correlations.
A Framework for Privacy and Security Requirements Analysis and Conflict Resolution for Supporting GDPR Compliance Through Privacy-by-Design

Requirements elicitation, analysis, and, above all, early detection of conflicts and resolution, are among the most important, strategic, complex and crucial activities for preventing software system failures, and reducing costs related to reengineering/fixing actions. This is especially important when critical Requirements Classes are involved, such as Privacy and Security Requirements. Recently, organisations have been heavily fined for lack of compliance with data protection regulations, such as the EU General Data Protection Regulation (GDPR). GDPR requires organisations to enforce privacy-by-design activities from the early stages and for the entire software engineering cycle. Accordingly, requirements engineers need methods and tools for systematically identifying privacy and security requirements, detecting and solving related conflicts. Existing techniques support requirements identification without detecting or mitigating conflicts. The framework and tool we propose in this paper, called ConfIs, fills this gap by supporting engineers and organisations in these complex activities, with its systematic and interactive process. We applied ConfIs to a realistic GDPR example from the DEFeND EU Project, and evaluated its supportiveness, with positive results, by involving privacy and security requirements experts (This research is an extension of the study conducted by Alkubaisy et al. [1] – which itself is a continuation of earlier studies [2, 3] and aims to aid the reader in comprehensively grasping the concepts laid out).
Twitter Sentiment Analysis using Supervised Machine Learning Techniques

Along with the evolution of time the large number of people used the social media platform to share views. This makes more people can communicate with each other. Alongside these benefits, it has some negative sides also which brings animosity towards some part of individuals. It can also include the hate speech. Hate speech is the speech that might include the abusive or threatening words which affects the community. Such type of speech need to be detected and removed from social media platform before spreading. Analysis of sentiments is the method of deciding whether the sentiments in the text is hatred or not hatred. We analyzed the Twitter dataset using weka software. In this dataset there were total 5000 Tweets and we applied two filters(Tweet to Sparse Feature Vector, Tweet to Lexicon Feature Vector) on it to give model accuracy of machine learning. The experimental result in both cases of Twitter dataset has an highest accuracy of Random forest technique i.e.93%.
Face Detection and Comparison Using Deep Learning

It is obvious that the crimes are increasing day by day in all the states in this country; we do believe that there are a lot which can be done by both the police officials and the people to reduce the crimes in states, so smart surveillance is used to find the criminal who is hiding from police and roaming in public places. This detector can be used in all kinds of public places like malls, cinema theaters and any crowded areas. The cameras in public places detect the faces; the system uses a combination of two methods; face recognition and verification. The face recognition is performed on acquired images, and then it verifies that face using a face classification method that uses convolution neural network which is integrated in the system. The system which is tested does the accurate performance to recognize the face within the requirement limits. After recognizing the face, it verifies that face through a criminal database. If the criminal is spotted, then the police will receive an alert in the control room like “criminal spotted” and then control room operators send the information to the nearest police to catch him. The police will receive the clear photo of that criminal, the location where he was spotted and information of his crime.
Quantum noise can enhance algorithmic cooling

Heat-Bath Algorithmic Cooling techniques (HBAC) are techniques that are used to purify a target element in a quantum system. These methods compress and transfer entropy away from the target element into auxiliary elements of the system. The performance of Algorithmic Cooling has been investigated under ideal noiseless conditions. However, realistic implementations are imperfect and for practical purposes, noise should be taken into account. Here we analyze Heat-Bath Algorithmic Cooling techniques under realistic noise models. Surprisingly, we find that noise can in some cases enhance the performance and improve the cooling limit of Heat-Bath Algorithmic Cooling techniques. We numerically simulate the noisy algorithmic cooling for the two optimal strategies, the Partner Pairing, and the Two-sort algorithms. We find that for both of them, in the presence of the generalized amplitude damping noise, the process converges and the asymptotic purity can be higher than the noiseless process. This opens up new avenues for increasing the purity beyond the heat-bath algorithmic cooling.
Abstractive Multi-document Summarization Using Deep Learning Approaches

Information in the digital documents has huge accumulation of data which is unstructured. Interpretation of any type of digital document having enormous unstructured data would be a tedious as well as time-consuming task and can also have content which is not necessary. Text summarization is the process of constructing precise and consistent information of a larger document. It is required to access the growing amount of the data to help in discovery of relevant information and for its faster utilization. The automatic summarization task is to develop certain techniques where the machine will be generating the summaries better than the written by human beings. Automatic summarization helps the users in many ways like reducing the reading time of the summary, making the process of selection easier in research, improvising the effectiveness, personalized summaries that can be useful in QA systems that provide individualized information, etc. Thus, the proposed model is aimed at making abstractive summaries of the documents that help the users to know the gist by the analyzing the content using approaches like natural language processing (NLP) and deep learning (DL). Abstractive summarization of multiple documents can be achieved by text summarization methods. Algorithms like Seq-to-Seq model and bidirectional long short-term memory encoder and decoder with attention mechanism (Bi-LSTM), Pointer-Generated network (PGN) and coverage mechanism (CM) are utilized. The disadvantages of Bi-LSTM like replacing one word with the other and having redundant words are the challenges that are addressed using PGN and CM. The proposed model will summarize the multiple documents and provides an abstractive summary that is relevant and precise.
GePpeTto Carves Italian into a Language Model

In the last few years, pre-trained neural architectures have provided impressive improvements across several NLP tasks. Still, generative language models are available mainly for English. We develop GePpeTto, the first generative language model for Italian, built using the GPT-2 architecture. We provide a thorough analysis of GePpeTto’s quality by means of both an automatic and a human-based evaluation. The automatic assessment consists in (i) calculating perplexity across different genres and (ii) a profiling analysis over GePpeTto’s writing characteristics. We find that GePpeTto’s production is a sort of bonsai version of human production, with shorter but yet complex sentences. Human evaluation is performed over a sentence completion task, where GePpeTto’s output is judged as natural more often than not, and much closer to the original human texts than to a simpler language model which we take as baseline.
RLSS: A Deep Reinforcement Learning Algorithm for Sequential Scene Generation

We present RLSS: a reinforcement learning algorithm for sequential scene generation. This is based on employing the proximal policy optimization (PPO) algorithm for generative problems. In particular, we consider how to effectively reduce the action space by including a greedy search algorithm in the learning process. Our experiments demonstrate that our method converges for a relatively large number of actions and learns to generate scenes with predefined design objectives. This approach is placing objects iteratively in the virtual scene. In each step, the network chooses which objects to place and selects positions which result in maximal reward. A high reward is assigned if the last action resulted in desired properties whereas the violation of constraints is penalized. We demonstrate the capability of our method to generate plausible and diverse scenes efficiently by solving indoor planning problems and generating Angry Birds levels.
Simulation of Robotic Arm Grasping Control Based on Proximal Policy Optimization Algorithm

Abstract There are many kinds of inverse kinematics solutions for robots. Deep reinforcement learning can make the robot spend a short time to find the optimal inverse kinematics solution. Aiming at the problem of sparse rewards in the process of deep reinforcement learning, this paper proposes an improved PPO algorithm. Firstly, built a simulation environment for the operation of the robotic arm. Secondly, use a convolutional neural network to process the data read by the camera of the robotic arm, obtaining a network about Actor and Critic. Thirdly, based on the principle of inverse kinematics of the robotic arm and the reward mechanism in deep reinforcement learning, design a hierarchical reward function containing motion accuracy to promote the convergence of the PPO algorithm. Finally, compare the improved PPO algorithm with the traditional PPO algorithm. The results show that the improved PPO algorithm has improved both the convergence speed and the operating accuracy.
Experimental quantification of dynamical coherence via entangling two qubits

Coherence and entanglement are both the fundamental properties which quantify the degree of nonclassicality possessed in a quantum state. Recently coherence and entanglement are considered as a dynamical resource where the nonclassicality is strongly related to the amount of the static resources which can be generated in a quantum process. In [Phys. Rev. Lett.125, 130401 (2020)10.1103/PhysRevLett.125.130401], for the first time, the authors study the interconvertability of these two kinds of dynamical resources. Here, we demonstrate this resource conversion in an all optical setup, and successfully observe the dynamical resource conversion. The experimental observation prove the ability of manipulating dynamical resource within current quantum photonic technologies.
Criteria to analyse the didactic communication strategies

A key factor of the educational process success is psychological competently built didactic communication between a student and teacher. The problem is only the lack of both methodological unity in the general, theoretical analysis of the phenomenon of didactic communication itself, and the patchwork of practices aimed to optimize the didactic communication real process. The study objective is to analyse the didactic communication criteria between a teacher and student, their understanding as the basis for selection and comparative analysis of different strategies of the teacher and student didactic communication. The paper carries out a meaningful analysis of the teacher-student didactic communication criteria: educational interaction key constructs, its different sides and aspects in the ideas of the teacher; communication space and time modes, «the learning effect concepts», the image of a student in teacher ideas, typical communication means; a didactic communication organizational form reproduced by a teacher; the teacher communicative self-identification and research tools, the didactic communication correction. As study results, the authors conclude that at an infinite variety of individual-personal shades of didactic communication, in general, it seems possible to single out historically changing common, covering most of the teaching community, systems of ideas on the optimal strategy of educational interaction with students.
“Tinkering is underrated”: librarians' use of single-board computers and microcontrollers outside of makerspaces

Purpose This research seeks to better understand the potential uses of maker technologies, such as single-board computers and microcontrollers, more broadly within libraries and not simply confined to the makerspace. Through interviews with librarians creating such projects, this study illustrates their successes, challenges, means of acquiring the necessary skills and knowledge, as well as their perceptions of the broader benefits and challenges to other library and information science practitioners. Design/methodology/approach This research study employed semistructured interviews with 12 librarians who have created projects with maker technologies for broader library use. Inductive qualitative analysis of the interview transcripts was conducted to identify themes of interest to the stated research questions. Findings Librarians' projects included: displaying digital signage, hosting online public access catalog stations, tallying reference desk interactions, counting patrons at the gate and monitoring 3-D printing statistics, among others. Participants appreciated the low-cost, flexible and creative nature of such technologies, and though they also encountered technical and organizational challenges in their use, relayed a potential series of benefits to librarians and library staff were these technologies to be more widely used. Originality/value Although significant research efforts have focused on aspects of makerspaces across all types of libraries, little work has formally collected and assessed library practitioners' work with maker technologies outside of the makerspace. Participants help detail the potential benefits of having a deeper understanding of this work, and the successes it could bring to librarians' work.
Correction to: Effectiveness of mobile SMS based counselling intervention in improving knowledge, attitude and practices of HIV/AIDS patients enrolled in hospitals/NGOs in Terengganu, Malaysia: a mixed mode study protocol

An amendment to this paper has been published and can be accessed via the original article.
A new identity of Dickson polynomials

A new polynomial identity is found for Dickson polynomials in characteristic 2. The identity is used to prove that the two polynomials xq+1+x+1/a and C(x)+a have the same splitting fields over F, where F is a field of characteristic 2, 0≠a∈F, q=2n>2, and C(x) is a Müller–Cohen–Matthews polynomial of degree (q2−q)/2. In addition, a new proof is obtained of the known result that C(x) induces a permutation on F2m if and only if 2m and n are relatively prime.
Lossy Compressor Preserving Variant Calling through Extended BWT

A standard format used for storing the output of high-throughput sequencing experiments is the FASTQ format. It comprises three main components: (i) headers, (ii) bases (nucleotide sequences), and (iii) quality scores. FASTQ files are widely used for variant calling, where sequencing data are mapped into a reference genome to discover variants that may be used for further analysis. There are many specialized compressors that exploit redundancy in FASTQ data with the focus only on either the bases or the quality scores components. In this paper we consider the novel problem of lossy compressing, in a reference-free way, FASTQ data by modifying both components at the same time, while preserving the important information of the original FASTQ. We introduce a general strategy, based on the Extended Burrows-Wheeler Transform (EBWT) and positional clustering, and we present implementations in both internal memory and external memory. Experimental results show that the lossy compression performed by our tool is able to achieve good compression while preserving information relating to variant calling more than the competitors. Availability: the software is freely available at https://github.com/veronicaguerrini/BFQzip.
An Investigation into the relationship between AI enabled Chabot Interface and Online buying behavior of Consumers in Delhi NCR Region

The rapid advancement in the field of artificial intelligence (AI) has offered tremendous opportunities to different organizations engaged in serving customers. AI provides a platter of add-ons that promise to enhance relationships with the stakeholders, to seamlessly upgrade and update marketing initiatives and to positively impact the consumer buying behaviour process. COVID-19 pandemic emerged as an immitigable disaster for many businesses; however, at the same time, online platforms gave way for a new edition of digital marketing. Thanks to the e-commerce websites consumers are now purchasing anything and everything online be it groceries or vegetables or clothes or washing machines. The need to physically visit a store to shop appeared to have weakened against the compulsion to shop from home during pandemic. However, studies have shown that given a chance, consumers will not trade-off traditional mode of shopping for online shopping, at least not completely. There still exists a desire to connect- physically, socially and emotionally- to buying process. In order impart personal touch to online shopping experience of the consumers a new AI enabled application, ‘Chatbot’, was developed. Chatbot assumes a human persona to enhance consumers’ online shopping experience, modify (amplify) their expectations related to product and assist them in making an informed purchase decision. Chatbots are a novel and trending artificial intelligence (AI) innovation. Through this study we attempt to investigate the impact of AI enabled Chatbots on pre-purchase, purchase and post-purchase behaviours of consumers in Delhi NCR region. The study proposes to investigate the relationship between consumers’ interface with AI enable chatbots and the impact thereof on pre-purchase, purchase and post-purchase behaviours. The implications of the study to marketers have also been suggested. The research employs correlation and regression techniques to test the research hypothesis on the Page primary data collected using a structured and undisguised questionnaire. The sample size is projected to be 200 respondents.