Reviewerly/exHarmony huggingface dataset:
Supervised Hashing with Recurrent Scaling

Learning to hash is a method that can deal with content-based information retrieval efficiently. Traditional learning to hash methods, however, lack the ability to map the generated hash codes to the high-level semantic space. Attributes, as a kind of higher level of visual data representation compared to features, have the potential ability in deep learning to boost the performance. Utilizing attributes from visual data in deep learning to hash can link every bit of the hash codes and a certain type of attributes, therefore giving the hash code an explicit explanation. This paper presents a novel framework, named Deep Recurrent Scaling Hashing (DRSH), to solve the traditional image retrieval problem. The hash codes generated from DRSH are a combination of the outputs of each step of an enhanced LSTM and features generated from convolutional neural nets and are learned through images’ attributes. This RNN is reformed to adjust the decorrelation of data flowing between each cell step, which not only makes the learning phase benefit from the ability of recurrent neural nets to learn with recurrent memory but also enable the availability of each hash bit to preserve distinct information. Experiments show that this framework can achieve appreciable performance on major datasets, and also have the ability to explain the meaning of hash codes based on attributes.
Cascade classifier as a tool for quick camera calibration

The mobile robotics development is very rapid nowadays. In the interest of companies and institutions is to transfer more and more task for mobile robots. The conversion from one application to other even similar is often associated with changes in hardware. The authors have performed a study witch effects possibility to significantly reduce the time of computer vision system calibration. The following paper presents multiple cameras calibration process for mobile robotics applications. The selection of applicable marker for cascade classifier training is described. Then the calibration of various cameras is shown. To perform this task the authors used application created by them especially for this. Results with emphasis for calibration errors are one of the most important parts of the paper. In the end, there are conclusions and prediction for further development and usage of this application.
Fast Nearest-Neighbor Classification Using RNN in Domains with Large Number of Classes

In scenarios involving text classification where the number of classes is large (in multiples of 10000 s) and training samples for each class are few and often verbose, nearest neighbor methods are effective but very slow in computing a similarity score with training samples of every class. On the other hand, machine learning models are fast at runtime but training them adequately is not feasible using few available training samples per class. In this paper, we propose a hybrid approach that cascades (1) a fast but less-accurate recurrent neural network (RNN) model and (2) a slow but more-accurate nearest-neighbor model using bag of syntactic features. Using the cascaded approach, our experiments, performed on data set from IT support services where customer complaint text needs to be classified to return top-N possible error codes, show that the query-time of the slow system is reduced to $$1/6^{th}$$ while its accuracy is being improved. Our approach outperforms an LSH-based baseline for query-time reduction. We also derive a lower bound on the accuracy of the cascaded model in terms of the accuracies of the individual models. In any two-stage approach, choosing the right number of candidates to pass on to the second stage is crucial. We prove a result that aids in choosing this cutoff number for the cascaded system.
Capitalization By Certification: Creating Information-Based Assets Through the Establishment of an Identification Infrastructure

The authors analyse the development and implementation of the standard for the Legal Entity Identifier as a case of creating information-based assets through the establishment of an infrastructure that certifies the accuracy and validity of identity data. The authors term this process capitalization by certification. The findings describe a process whereby an identification infrastructure – including a non-replicable methodology for assessing data quality – is established that contributes to making the developer and controller of that methodology, an irreplaceable intermediary for users of the infrastructure; this in spite of the need for an associated reference data infrastructure to be open and widely accessible to all participants for the infrastructure to be successful. The findings indicate that in the process, assets are created on the basis of openly accessible data through certifying of a desired set of qualities to be achieved by adopters and the infrastructure. This, in turn, provides a starting point toward better understanding and theorizing of wider processes of data capitalization, encountered throughout the digital economy but which are also crucial to establishing information infrastructures that support cognitive action.
A Mobile Quality-oriented Cooperative Multimedia Delivery Solution

Mobile video traffic is rapidly growing, putting significant pressure on current heterogeneous wireless networks. Common data is traditionally requested by multiple users from server or cache devices. This results in the same data being sent across the network multiple times causing unnecessary congestion. The proposed cooperative solution allows neighbouring devices to share content that was previously received with interested peers. In this paper, a Mobile aware Quality-oriented Cooperative Multimedia Delivery Solution is proposed which allows peer devices to identify neighbouring host devices while considering their own mobility. Simulated testing shows that the solution is capable of identifying the most suitable host while travelling at different speeds and maintaining a suitable quality level by adapting the content to meet network conditions. State-of-the-art comparative studies are outperformed by maintaining good quality with increasing speed in a mobile environment.
Vendor-independent soft tissue lesion detection using weakly supervised and unsupervised adversarial domain adaptation

Computer-aided detection aims to improve breast cancer screening programs by helping radiologists to evaluate digital mammography (DM) exams. DM exams are generated by devices from different vendors, with diverse characteristics between and even within vendors. Physical properties of these devices and postprocessing of the images can greatly influence the resulting mammogram. This results in the fact that a deep learning model trained on data from one vendor cannot readily be applied to data from another vendor. This paper investigates the use of tailored transfer learning methods based on adversarial learning to tackle this problem. We consider a database of DM exams (mostly bilateral and two views) generated by Hologic and Siemens vendors. We analyze two transfer learning settings: 1) unsupervised transfer, where Hologic data with soft lesion annotation at pixel level and Siemens unlabelled data are used to annotate images in the latter data; 2) weak supervised transfer, where exam level labels for images from the Siemens mammograph are available. We propose tailored variants of recent state-of-the-art methods for transfer learning which take into account the class imbalance and incorporate knowledge provided by the annotations at exam level. Results of experiments indicate the beneficial effect of transfer learning in both transfer settings. Notably, at 0.02 false positives per image, we achieve a sensitivity of 0.37, compared to 0.30 of a baseline with no transfer. Results indicate that using exam level annotations gives an additional increase in sensitivity.
Unified Group Recommendation Towards Multiple Criteria

In online social networks, a growing number of people are willing to share their activities with ones who have common interests. This motivates the research on group recommendation, which focuses on the issue of recommending items to a group of users. The existing methods on addressing the problem of grouping users and making recommendations for the formed groups simultaneously, however, often suffer from two defects. The first one is that they separate group partition and group recommendation, which often reduce the overall group satisfaction. The second one is that they tend to pursue a single objective optimum instead of making a balance between multiple objectives. In this paper, we strive to tackle the key problem of grouping users and making recommendations for the formed groups simultaneously. It is a challenging problem due to the differences between user preferences over items, and how to make a trade-off among their preferences for the recommended items is still the main research point. To address these challenges, we present a Unified Group Recommendation (UGR) model, which intertwines the user grouping and group recommendation in a unified multi-objective optimization process that makes a balance between multiple criteria, including maximizing overall group satisfaction, social relationship density, and overall group fairness. Extensive experiments on two real-world datasets verify the effectiveness of our method.
InferSpark: Statistical Inference at Scale

The Apache Spark stack has enabled fast large-scale data processing. Despite a rich library of statistical models and inference algorithms, it does not give domain users the ability to develop their own models. The emergence of probabilistic programming languages has showed the promise of developing sophisticated probabilistic models in a succinct and programmatic way. These frameworks have the potential of automatically generating inference algorithms for the user defined models and answering various statistical queries about the model. It is a perfect time to unite these two great directions to produce a programmable big data analysis framework. We thus propose, InferSpark, a probabilistic programming framework on top of Apache Spark. Efficient statistical inference can be easily implemented on this framework and inference process can leverage the distributed main memory processing power of Spark. This framework makes statistical inference on big data possible and speed up the penetration of probabilistic programming into the data engineering domain.
SeeIME

For foreign Chinese learners, learning Pinyin is much easier than Chinese characters. Traditional Chinese input methods (IMEs) that are intended for Chinese people are not suitable for Chinese learners whose mother language is non-Chinese. In general, different languages can be translated into each other. By this feature and the relevant knowledge of existing IMEs, we take Chinese and English as an example to propose a hybrid encoding that combines Pinyin and the first two letters of its translation. We implement SeeIME with this hybrid Pinyin code. SeeIME can effectively reduce the number of candidate words and help users recognize Chinese characters better via a three-block display method. Using this code for sentence generation can improve the hit rate, which increases from 58% to 84%. The implemented IME is already available for the Android system.
Protection from reconnaissance for establishing information security systems

The paper presents a generalized method for improving security of information systems based on protection of the systems from reconnaissance by adversaries. Attacks carried out by exploiting almost all vulnerabilities require particular information about the architecture and operating algorithms of an information system. Obstructions to obtain that information also complicates carrying out attacks. Reconnaissance-protection methods can be utilized for establishing such systems (continuous change of attack surface). Practical implementation of the techniques demonstrated their high efficiency in reducing the risk of information resources to be cracked or compromised.
ANALISIS KUALITAS PELAYANAN PADA RUMAH SAKIT UMUM (RSUD) DUMAI

The Regional General Hospital
A Survey on Knowledge Extraction Techniques for Web Tables

Web tables are worthy sources of relational information. The number of high-quality tables with useful relational information is rapidly increasing to hundreds of millions. Some search engines usually ignore meanings of entities and relationships in indexing thus they have poor performance in tabular data to a suitable field of research is the transformation of web tables into machine-readable knowledge. We first study overview of the use of web tables in different domains then focus on understanding knowledge of web tables. The results indicate that by combining old Information Extraction techniques, and table features and general inference models can extract Knowledge from web tables.
Reducing Pixel Range to Improve Image Compression Quality

Objective: This study aims to contribute in developing a compression method that performs both high quality and applicable compression ratio, which is a hard task and requires a lot of sequential experiments since each of the aimed characteristics belong to different group type of image compression techniques lossy and lossless. Therefore, the hybridization between those two types is the convenient solution because it allows the desired results to appear in one technique. Methods/Findings: The researcher proposed reducing the pixel range that represents the original image by a fixed rate, which yields to reducing the repetition and preserving a large amount of the data in this phase before proceeding to the lossy part, in this case, RIFD technique that will affect better reducing the compressed image size due to the previous step. The Calculations of the objective measurements MSE, MAE and PSNR used to assess the effect of the suggested method (RPR) by collecting the results which gained after applying it to different grayscale and colour images. Application: The proposed lossless part does not require complex calculations and the results proved its ability to improve the compression quality for the elected lossy technique without affecting the compression ratio. Comparisons showed significant improvement on grey scale images especially those having 16-bit depth and marginally in RGB images. Keywords: Compression Quality, Hybrid Compression, Objective Metrics, Reducing Pixel Range, Redundancy, RIFD Technique
Watching People Talk; How Machines Can Know We Understand Them—A Study of Engagement in a Conversational Corpus

This paper describes an examination of the HuComTech Corpus of multimodal interactions from the viewpoint of an automated dialogue system. Specifically, it looks at the difference between formal and informal interactions from the point of view of image processing. We show that an autonomous dialogue system is able to make inferences about the engagement states of its interlocutor in order to function efficiently without complete dialogue understanding. The paper shows that a very simple image processing algorithm is capable of distinguishing engagement states with a high degree of reliability. We can infer from the results that future autonomous agents might use a similar method to estimate the degree of engagement a human interlocutor has in their conversations.
DeepFuse neural networks

Generally, pre-trained backbone convolutional models for image classification are directly used as default backbone model for other tasks including detection and segmentation, so as to avoid training a new model from scratch. However, segmentation and detection frameworks need combining low-level and high-level features to boost performance. In this paper, we point out and prove that a simple fusion of low-level and high-level features is insufficient and defined an operation named DeepFuse as a generic family of building blocks for feature fusion. This building block can be plugged into any existing architectures and prevent overfitting effectively for region-level and pixel-level tasks. Based on the Mask R-CNN framework, we achieved a 1.9 AP improvement on the validation set of cityscape and a 1.7 AP improvement on the test set by the use of DeepFuse operation.
On IND-CCA1 Security of Randomized McEliece Encryption in the Standard Model

We show that a modification of Nojima et al.’s Randomized McEliece Encryption (RME), where receiver verifies the correctness of the decrypted plaintext, achieves IND-CCA1 security in the standard model. We rely on the two (standard) assumptions used also for RME: hardness of general decoding and Goppa code indistinguishability (sometimes they are jointly referred to as “the McEliece assumptions”), plus an extra assumption on non-falsifiability of the McEliece ciphertexts. The later one implies that an adversary is unable to sample a McEliece ciphertext for which the message and error vector are unknown. This assumption is non-standard, however it represents a win-win argument, in the sense that breaking it would imply efficient sampling of McEliece ciphertexts, which in turn may potentially lead us to a Full Domain Hash code-based signature based on the McEliece PKE—without rejection sampling as in the Courtois-Finiasz-Sendrier signature from Asiacrypt 2001—a long-standing open problem in code-based cryptography.
Evaluation of Causal Arguments in Law

In many legal disputes, determining and evaluating cause-in-fact is a crucial step in the liability attribution. It is, however, difficult and opaque. In this paper, we analyse the cases of overdetermination, where there is more than one cause for the outcome. The proposed framework (FCA) employs logic-based argument modelling. It distinguishes individual contributors in overdetermination cases by using a new set of critical questions based on argument schemes from effect-to-cause. To illustrate the use of the FCA, the Heneghan v Manchester Dry Docks lung cancer case with multi-party contributions is analysed.
The implementation of trithemius algorithm and modified least significant bit (mlsb) blue channel for bitmap image security

Abstract In the delivery of information confidentiality message is one of the things that are very important, because the image data is very span to the actions of computer crimes such as modified or stolen. The combination of cryptography and steganography can be one solution to prevent such crime. Here the author uses the Trithemius algorithm. Trithemius algorithm is a symmetric algorithm that has advantages that is in the process of encryption or decryption of symmetric cryptography takes a short time and key size relatively shorter. While the weakness is the key must be changed frequently, every time implementing communication for the security of the message so that the authors combine with steganography technique that is Modified Least Significant Bit (MLSB), so that messages will be inserted not visible clearly or visible. In this research, the message or image file will be sent is first encrypted with Trithemius cryptography algorithm, then encryption result will be hidden in an image file with bitmap format using Blue Channel MLSB steganography method. Then do the process of extraction of messages for further decryption to return to the original message as before.
Record Management in the Cloud: Service Quality and Service Level Agreement

Recently, many companies or organizations are introducing cloud services for managing digital records. Cloud services can dramatically reduce the cost of archiving and managing digital records, and provide a foundation for resilient management of digital records, depending on the business environment. Many companies and organizations outsource management of digital records to the cloud. Generally, companies sign SLA contracts when outsourcing computing systems to the cloud. This means that they will pay for the usage of the service while expecting cloud service more or less quantitative level. Customers who request cloud record management services want to be assured that their records are kept in trust in the cloud while meeting records requirements. The cloud record management service provider wants to know which quality items or criteria associated with records should be used to manage records. To establish a cloud record management service SLA, customers and service providers must be aware of each other’s interrelationships, make a clear statement of their requirements and expectations, and agree on them. Because cloud services pay for service usage and satisfaction, the SLA must be defined for the purpose so that the customer and service provider can provide services that are mutually satisfactory. This paper describes the requirements for ensuring the quality of cloud record management services and the quality items and classification schemes and key indicators and values required for establishing SLAs.
A secret classified label control model based on the identity-based cryptography

Aiming at the defects of complex certificate management and high waste of resources in the traditional secure electronic file label management system, a secret classified label (SCL) control model was established based on the identity-based cryptography (IBC). In the secure controlling model of SCL, the digital signature method and the hierarchical access method of electronic documents based on the identity-based cryptography are used to digitally sign the confidential labels and encrypt the confidential documents respectively. Through the modeling of confidential clients, file servers, and key generation centers, the irreproachability of file-level identification and the hierarchical access to files are achieved. Finally, the feasibility, practicability and security of the model are verified by testing the system performance and analyzing the system security.
Inference for conditioned Galton-Watson trees from their Harris path

Tree-structured data naturally appear in various fields, particularly in biology where plants and blood vessels may be described by trees, but also in computer science because XML documents form a tree structure. This paper is devoted to the estimation of the relative scale parameter of conditioned Galton-Watson trees. New estimators are introduced and their consistency is stated. A comparison is made with an existing approach of the literature. A simulation study shows the good behavior of our procedure on finite-sample sizes and from missing or noisy data. An application to the analysis of revisions of Wikipedia articles is also considered through real data.
MedlinePlus at 21: A website devoted to consumer health information

This report is on the consumer health website, MedlinePlus.This website was created by the National Library of Medicine and features content produced by the National Institutes of Health.This report covers an overview, origin, content and possible future evolution of this website.The report also spotlights the specific features of the MedlinePlus health topic on health literacy and covers in more detail the
Mathematical Analysis of Image Information Retained in the Complex Domain Phases Under Additive and Multiplicative Noise

It is often observed that phases in complex transform of images are important ingredients compared to their magnitude for information extraction. Literature indicates that these phases are immune to noise. The aim of this paper is to find the structural (Edge) and statistical information retained by the complex domain phases for the images corrupted with additive white Gaussian noise (AWGN) and multiplicative (speckle) noise. Initially, we measure the edge information preserved by both phase and magnitude only synthesized image using edge mismatch error (EMM), to illustrate the significance of phase in image restoration and reconstruction. A mathematical model for the sensitivity of phase and magnitude is derived to examine the respective rate of deterioration under varying noise strength. Both the mathematical finding and experimental results indicate that the phase of any complex transform is degraded slowly compared to its magnitude. Comparative analysis of effect of noise on these phases is also investigated and reported.