Reviewerly/exHarmony huggingface dataset:
Interpretable Encrypted Searchable Neural Networks

In cloud security, traditional searchable encryption (SE) requires high computation and communication overhead for dynamic search and update. The clever combination of machine learning (ML) and SE may be a new way to solve this problem. This paper proposes interpretable encrypted searchable neural networks (IESNN) to explore probabilistic query, balanced index tree construction and automatic weight update in an encrypted cloud environment. In IESNN, probabilistic learning is used to obtain search ranking for searchable index, and probabilistic query is performed based on ciphertext index, which reduces the computational complexity of query significantly. Compared to traditional SE, it is proposed that adversarial learning and automatic weight update in response to user’s timely query of the latest data set without expensive communication overhead. The proposed IESNN performs better than the previous works, bringing the query complexity closer to $$O(\log N)$$ and introducing low overhead on computation and communication.
Stabilizer codes from modified symplectic forms

In this paper, we construct explicit families of quantum error correcting codes based on cyclic codes. But for a crucial theoretical idea that we introduce here, namely working with a modified symplectic form as the basis of Weyl commutation, these codes would not have been possible - under the standard symplectic form, there are Galois theoretic no-go theorems on cyclic codes [7], [Corollary IV.5, page 651], [6], [Corollary 5.16, page 51] of certain lengths. More importantly, we circumvent the above no-go theorems without disturbing the one crucial property that cyclic codes have - efficient decoding algorithms. Recall that for general codes, efficient decoding algorithms do not exist if some widely believed complexity theoretic assumptions are true hence this is a crucial property to have. Cyclicity is a basis dependent property and for codes that we construct, is only evident in the modified symplectic setting. If we were to recast this code using the standard symplectic form, there would be a basis change on the underlying vector space that disturbs cyclicity. Hence, this change of perspective that we have here is crucial not only in the construction, where we had to circumvent the no-go theorems that we mentioned above, but also in the design of efficient decoding algorithm. Theoretically our result is also tight: we have a complete characterization (Theorem 6) of the codes that we construct here.
Information Technology for Supporting Self-Control in the Formation of a Rational Lifestyle for Diabetics Patients

Introduction. Modern Diabetes mellitus is dangerous, chronic endocrine disease that originates from the disorder of metabolism, connected primarily with violation of carbohydrate exchange. Providing the necessity of independent self-control of health status of diabetes patients is the urgent problem of present time. The use of information technologies and mobile medicine facilitates enhancing the efficiency of self-control of health status by the patient.The purpose of the paper is to develop a combined information technology to enhance the efficiency of glycemic self-control in case of diabetes at different stages of treatment.
Scalable Security Based on Data Classification Using Generalized RSA in Cloud Storage

In cloud environment, huge amount of data is stored in data centers. These data are generated from numerous sources such as social media, social networking and others. These data are generally in form of text, image, audio or video. These data are exponentially increasing due to huge amount of multiple streams generating these data at every millisecond. Many of these data are permanently stored in data centers over cloud and rarely used. However, all of the data stored in data centers over cloud are encrypted using some encipherment mechanisms for providing security objectives. There are several mechanisms used for encipherment such as DES, RSA etc. Using any of these encipherment mechanisms involve huge amount of cost. The cost is usually borne by the service provider. In this paper, we devised a novel method for scalable security based on data classification using Generalized RSA-AA using 2n prime numbers. The objective of proposing a scalable security model based on Generalized RSA-AA. In this paper, a flow model has been presented discussing the encipherment of data as per their classification. The security imposed on the data can also be scaled up to desired security strength based on the requirement of the user or the importance of data.
An analysis of mobile pass-codes in case of criminal investigations through social network data

In today’s modern world, mobile has turned out to be one of the essentials for all the people irrespective of their status and profession. With the help of this device, all the data about a person can be tracked down (i.e. from the diet, diseases to contacts and transactions). In case of criminal investigations, inspectors need to collect information about a victim or accused. For this, the individual’s mobile phone plays a vital role. However, it is very difficult to access a device without its owner’s permission. Suppose, if the victim is dead or not willing to expose the information, the cyber police should perform many complex tasks to retrieve the data. Thus, there is a great need to analyze this task and make it feasible to find out pass-codes in order to access the mobile device. This paper explains how passwords can be cracked with ease with the help of survey and training of large dataset. We all know that people these days are very active on social media, which makes it easy to track them. In this work, we analyze a few passcodes and patterns and try to test that data by giving the queries. Here, we can analyze and retrieve the data from an individual’s social media such as date of birth, name, personal information and try to predict the passcode in very few attempts as it turns out that majority of the time, the passcode is generally predictable based on some key characteristics identified in this paper.
A Novel Image Steganography Model Using LSB with Extended ASCII Codes

Steganography is a method for invisible communication while concealing the actual data to be transmitted within non-secret data or text. It is the process of hiding the secret information within another form of data like text, image, audio, or video. Image steganography intends to embed a secret message into another image, called as a cover image through an encrypting algorithm, and the secret data can be retrieved by using a decrypting algorithm. The two major techniques used in image steganography process are spatial domain and transform domain. LSB is a commonly used technique as the payload capacity is higher compared to transform domain techniques, whereas the robustness is lower. There are various other models where the disadvantages of each type are being reduced. But the proposed model is a hybrid model which would increase payload capacity as well as robustness with a strong security. This helps to combine the advantages of each type of image steganography. We can do so by replacing the common words by using special symbols with extended ASCII values.
Image Enhancement Technique based on Power-Law Transformation of Cumulative Distribution Function

This paper addresses the problem of real-time image enhancement in automatic mode in mobile and wireless applications. A new technique of image enhancement by intensity transformation based on an adaptive power-law transformation of the cumulative distribution function of brightness is proposed. The proposed technique provides an effective increase in the image contrast without the emergence of unwanted artifacts and has several advantages compared with the well-known technique of histogram equalization. The results of the research confirm the effectiveness of the proposed technique for improving images in automatic mode.
Detecticon: A Prototype Inquiry Dialog System

A prototype inquiryHiraoka, Takuya dialogMotoura, Shota system, dubbedSadamasa, Kunihiko Detecticon, demonstrates its ability to handle inquiry dialogs, including presenting reasonable arguments, tracking user beliefs, and refining its arguments. User interaction is supported by using a natural language interface and a graphical user interface.
Perfect Codes in Euclidean Lattices: Bounds and Case Studies

In the present paper, we investigate the existence of lattice perfect codes when considered as sublattices of other lattices under the Euclidean metric. We generalize bounds on the radius of perfect codes in a generic lattice, previously known for the cubic lattice. The new bounds are based on covering density, and covering radius of the ambient lattices, and, along with algebraic methods, allow to characterise all perfect codes in small dimension for a given ambient lattice. We provide case studies for some well known ambient lattices, such as the hexagonal lattice, and the checkerboard lattices. In contrast to the cubic lattice, these case studies show that, by changing the ambient lattice, one can find rich sets of perfect codes.
Asymptotics of Entropy of the Dirichlet-Multinomial Distribution

Dirichlet distribution and multinomial distribution play important role in information theory and statistics. They find applications in estimation, average minimax redundancy in source coding, Pólya urn model, and graph compression. Dirichlet-multinomial distribution is a multinomial distribution in which parameters are distributed according to the Dirichlet distribution. In this paper, we present some characteristics of the Dirichlet-multinomial distribution, including a precise asymptotic for the entropy. It should be point out that such a characterization turns out to be technically quite challenging requiring analytic tools including analytic continuation of hypergeometric series.
Norbert Hornstein, Howard Lasnik, Pritty Patel-Grosz and Charles Yang (eds.):<i>Syntactic Structures</i>after 60 years. The impact of the Chomskyan revolution in linguistics

"Norbert Hornstein, Howard Lasnik, Pritty Patel-Grosz and Charles Yang (eds.): Syntactic Structures after 60 years. The impact of the Chomskyan revolution in linguistics" published on Sep 2019 by Akadémiai Kiadó.
Discovering Implicit Quantity Relations Using Convolutional Network

This paper presents a novel approach for automatically extracting implicit quantity relations (IQRs) from Chinese algebra word problems (AWPs). Our algorithm first classifies AWPs into categories and analyzes each sentence in the problem to identify the variables and values, then maps them to equations according to the category, and enables its trivial as shown in Figure 1. Our algorithm is able to discover 15 kinds of IQRs in AWPs with an average accuracy of 88% when the training sample accounts for more than 3% of the total. Our data and code are publicly available.
Time-universal data compression and prediction

Suppose there is a large file which should be transmitted (or stored) and there are several (say, m) admissible data-compressors. It seems natural to try all the compressors and then choose the best, i.e. the one that gives the shortest compressed file. Then transfer (or store) the index number of the best compressor (it requires [log m] bits) the compressed file. The only problem is the time, which essentially increases due to the need to compress the file m times (in order to find the best compressor). We propose a method that encodes the file with the optimal compressor, but uses a relatively small additional time: the ratio of this extra time and the total time of calculation can be limited by an arbitrary positive constant. A similar situation occurs when forecasting time series.Generally speaking, in many situations it may be necessary find the best data compressor (or predictor) out of a given set, which is often done by comparing them empirically. One of the goals of this work is to turn such a selection process into a part of the data compression method, automating and optimizing it.
TQ-Model: A New Evaluation Model for Knowledge-Based Authentication Schemes

Many user authentication schemes are developed to resolve security issues of traditional textual password scheme. However, only Android unlock scheme gets wide acceptance among users in the domain of smartphones. Although Android unlock scheme has many security issues, it is widely used due to usability advantages. Different models and frameworks are developed for evaluating the performance of user authentication schemes. However, most of the existing frameworks provide ambiguous process of evaluation, and their results do not reflect how much an authentication scheme is strong or weak with respect to traditional textual password scheme. In this research paper, an evaluation model called textual passwords-based quantification model (TQ-Model) is proposed for knowledge-based authentication schemes. In the TQ-Model, evaluation is done on the basis of different features, which are related to security, usability and memorability. An evaluator needs to assign a score to each of the feature based on some criteria defined in the model. From the evaluation result, the performance difference between a knowledge-based authentication scheme and textual password scheme can be measured. Furthermore, evaluation results of Android unlock scheme, picture gesture authentication scheme and Passface scheme are presented in the paper using the TQ-Model.
Accelerating Big Data Analytics Using Scale-Up/Out Heterogeneous Clusters

Production data analytic workloads typically consist of a majority of jobs with small input data sizes and a small number of jobs with large input data sizes. Recent works advocate scale-up/scale-out heterogeneous clusters (in short Hybrid clusters) to handle these heterogeneous workloads, since scaleup machines (i.e., adding more resources to a single machine) can process small jobs faster than simply scaling out the cluster with cheap machines. However, there are several challenges for job placement and data placement to implement such a Hybrid cluster. In this paper, we propose a job placement strategy and a data placement strategy to solve the challenges. The job placement strategy places a job to either scale-up or scale-out machines based on the job's characteristics, and migrates jobs from scale-up machines to under-utilized scale-out machines to achieve load balance. The data placement strategy allocates data replicas in the two types of machines accordingly to increase the data locality in Hybrid cluster. We implemented a Hybrid cluster on Apache YARN, and evaluated its performance using a Facebook production workload. With our proposed strategies, a Hybrid cluster can reduce the makespan of the workload up to 37% and the median job completion time up to 60%, compared to traditional scale-out clusters with state-of-the-art schedulers.
A novel model of feature extraction for lung cysts detection in CT image using Minutiae based Mumford and Shah functional model

Lung cancer is one of the commonly occurring and most hazardous diseases to cure which increases the death rate day by day. In order to reduce the death rate, it is necessary to detect the lung cancer in its initial stages and thereby to assist the surgeons to clear away the portion of lung for the treatment of lung cancer, and tumours. This paper concentrates at developing a Computer-Aided Diagnosis (CAD) system for detecting lung cancer by analysing the Computed Tomography (CT) images of lungs. And it is carried out with filtering, binarization, image segmentation based on the Mumford and Shah model, Image enhancement including binarization and thinning, Minutiae Extraction using Termination and Bifurcation, Removal of False Minutiae Points and finally feature Extraction using grey level co-occurrence matrix (GLCM). Also, the system removes 98% of false minutiae and thus, is more efficient than other algorithms which achieve 80% to 90%. In addition, the image quality is analysed using various assessment Metrics. Finally, a comparative and robustness analyses are carried out with the existing self-learning approach in terms extracted values. The results of the proposed system show that there is significant improvement in PSNR value compared to the existing method.
Management of professional training

The planned modernization of the higher education system should be based on the analysis of the initial state of the system at the present time, problems encountered in recent years and suggestions from all stakeholders. The quality of education of graduates (level of competence) depends on the organization and implementation of the educational process (the contribution of the University) and the activity of students (the contribution of students). Considered the expediency of using the results of student’s surveys to adjust the educational process. Shown that the educational program can take into account the propensity of students to a particular type of activity (technologist, organizer, researcher). Determining the causes of students ’ anxiety helps to improve the educational process and relations with students. Argued that the quality of education increases with earlier adaptation of students to the educational environment of the University. It is shown how the degree of formation of volitional qualities of students affects the manifestation of such properties as responsibility, discipline, organization and diligence. The success of professional activities of graduates of educational organizations will be determined by the direction of their life goals. The assessment of life goals of students shows the need for continuous improvement of socio-cultural (educational) environment in universities. Measures for improvement of educational process and improvement of pedagogical qualification of teachers are offered.
Behind the starbucks counter

Virtual reality (VR) shows great potential for enabling real-time embodied interactions among multiple people. Despite such potential, few VR studies have investigated the collaborative training of a small group, especially performing hands-on tasks while sharing the limited size of physical space. This experience report explores how a VR environment can accommodate collaborative training occurring in a coworking space by examining Starbucks cafes as a case study. Based on the findings, the author suggests design solutions for the VR environment and possible interaction strategies regarding the suggested design. The current report is an initiating attempt to provide insight into utilizing a novel technology for better collaborative training.
Extending the Transformer with Context and Multi-dimensional Mechanism for Dialogue Response Generation

The existing work of using generative model in multi-turn dialogue system is often based on RNN (Recurrent neural network) even though the Transformer structure has achieved great success in other fields of NLP. In the multi-turn conversation task, a response is produced according to both the source utterance and the utterances in the previous turn which are regarded as context utterances. However, vanilla Transformer processes utterances in isolation and hence cannot explicitly handle the differences between context utterances and source utterance. In addition, even the same word could have different meanings in different contexts as there are rich information within context utterance and source utterance in multi-turn conversation. Based on context and multi-dimensional attention mechanism, an end-to-end model, which is extended from vanilla Transformer, is proposed for response generation. With the context mechanism, information from the context utterance can flow to the source and hence jointly control response generation. Multi-dimensional attention mechanism enables our model to capture more context and source utterance information by 2D vectoring the attention weights. Experiments show that the proposed model outperforms other state-of-the-art models (+35.8% better than the best baseline).
Thematic Learning-based Full-text Retrieval Research on British and American Journalistic Reading

As for Journalistic Reading Course teaching, it is rather difficult to retrieve instructive and valuable ones from massive online news. In combination with the actual course requirements, the paper endeavors to adopt thematic learning as a means and attach more importance to such three weight indicators as news title, length and timeliness to redesign weight function on the basis of Lucene full-text retrieval algorithm. The comparative experiments prove that the respective addition of length weight, title weight and timeliness weight guarantees the retrieval precision ratio of the top ten improved by 43.6%, 69.2% and 35.9% than before, and by 94.9% after a simultaneous addition of these three weights. It verifies that the search result of the top ten after improvement is more in line with actual teaching requirements in terms of news length and timeliness.
The Path to Digitalisation in ELT Classes

This chapter presents the history of digitalisation in language teaching and learning. History of computers in language classes is examined since the introduction of computer technology in language teaching up to now. The interaction between language teaching and computers is presented in a timeline to get a clear picture about the great evolution of the role of computers from a drilling machine into creating a virtual classroom atmosphere. Starting from 1970s, history of computer-assisted language learning is depicted to enlighten the idea of how social networking has emerged and how it is used in language teaching. After that, social nature of learning and its role in digitalisation is explained within theoretical framework of behaviourist and constructivist approaches.
The Mirrored Body

Real-time video processing in multimedia performances can create a video double that recalibrates understandings of both on screen and physical bodies. This mirrored bodily exchange affords new experiences of agency, sensation and expression in the context of theatrical performances. Careful attention is paid to creating a life-sized mirror image that places the processed body in close relationship with the living body. These video processing techniques find their purpose as characters in storytelling for theatre and dance.
Learning to Predict Image‐based Rendering Artifacts with Respect to a Hidden Reference Image

Abstract Image metrics predict the perceived per‐pixel difference between a reference image and its degraded (e. g., re‐rendered) version. In several important applications, the reference image is not available and image metrics cannot be applied. We devise a neural network architecture and training procedure that allows predicting the MSE, SSIM or VGG16 image difference from the distorted image alone while the reference is not observed. This is enabled by two insights: The first is to inject sufficiently many un‐distorted natural image patches, which can be found in arbitrary amounts and are known to have no perceivable difference to themselves. This avoids false positives. The second is to balance the learning, where it is carefully made sure that all image errors are equally likely, avoiding false negatives. Surprisingly, we observe that the resulting no‐reference metric, subjectively, can even perform better than the reference‐based one, as it had to become robust against mis‐alignments. We evaluate the effectiveness of our approach in an image‐based rendering context, both quantitatively and qualitatively. Finally, we demonstrate two applications which reduce light field capture time and provide guidance for interactive depth adjustment.