Reviewerly/exHarmony huggingface dataset:
Clinical digital neuropathology: experience and observations from a departmental digital pathology training programme, validation and deployment

Aim To train and individually validate the neuropathologists in digital primary diagnosis and frozen section reporting using a novel protocol endorsed by the Royal College of Pathologists. The protocol allows early exposure to live digital reporting in a risk mitigated environment. Methods Two specialist neuropathologists completed training in the use of a digital microscopy system for primary neuropathological diagnosis and frozen section assessment. Participants were exposed to training sets of 20 histology cases and 10 frozen sections designed to help them identify their personal digital diagnostic pitfalls. Following this, the pathologists viewed 340 live, complete neuropathology cases. All primary diagnoses were made on digital slides with immediate glass slide reconciliation before final case sign-out. Results There was 100% clinical concordance between the digital slide and glass slide assessment of frozen section cases for each pathologist, and these assessments corresponded with the ground truth diagnoses obtained from examination of definitive histology. For primary diagnosis, there was complete clinical concordance between digital slide and glass slide diagnosis in 98.1% of cases. The majority of discordances were related to grading differences attributable to mitotic count differences. Conclusion Neuropathologists can develop the ability to make primary digital diagnosis competently and confidently following a course of individual training and validation.
Classification of the temperature-dependent gain of an erbium-doped fiber amplifier by using data mining methods

In this study, the experimental results of an erbium-doped fiber amplifier (EDFA) system designed and experimentally installed in the C-band were classified by using data mining methods. The non-linearity of the EDFA gain with temperature is a problem for EDFA designers. For this reason, the wavelength range to be used in the design, the temperature values and change in the input power values of signals coming to the design are not linear with respect to the output power. Data mining method based on different models are proposed to solve this problem. The data set used contains four variables: temperature, wavelength, input power, and output power. The success results of at WEKA software (Waikato environment for knowledge analysis) were compared with 6 different algorithms. As a result of the analysis made, the C4.5 algorithm, which is the decision tree classification technique, obtained 98.4 % close results in experimental results. When the results are evaluated, data mining algorithms can be constructed and validated using larger data sets. This will help the fiber optic data to be quickly understood and easy to design for optical designers in practical applications.
Structured Compression of Deep Neural Networks with Debiased Elastic Group LASSO

State-of-the-art Deep Neural Networks (DNNs) are typically too cumbersome to be practically useful in portable electronic devices. As such, several works pursue model compression that seeks to drastically reduce computational memory footprints, FLOPS and memory for storage. Many of these works achieve unstructured compression, where the compressed models are not directly useful since dedicated hardware and specialized algorithms are required for storage of sparse weights and fast sparse matrix-vector multi-plication respectively. In this paper, we propose structured compression of large DNNs using debiased elastic group LASSO (DEGL), which is motivated by different interesting characteristics of the individual components. That is, where group LASSO penalty enforces structured sparsity, l2-norm penalty promotes features grouping, and debiasing disentangles sparsity and shrinkage effects of group LASSO. We perform extensive experiments by applying DEGL to different DNN architectures including LeNet, VGG, AlexNet and ResNet on MNIST, CIFAR-10, CIFAR-100 and ImageNet datasets. Furthermore, we validate the effectiveness of our proposal on domain adaptation using Oxford-102 flower species and Food-5K datasets. Results show that DEGL can compress DNNs by several folds with small or no loss of performance. Particularly, DEGL outperforms conventional group LASSO and several other state-of-the-art methods that perform structured compression.
Swarm Robotics in Disaster Management

Cooperation has been one of the most used concepts that has been implemented in multi-robot systems for performing various complex tasks. Swarm robotics is where a group of functional robots come together and perform a coordinated action based on the type of algorithm. Due to its robust and flexible nature swarm robots have been used in dynamic environments where access otherwise is a difficult task. Disaster response teams now deploy robotics along with human force where the teams rely on the inputs of the robots. The robustness and the automation feature of this technology makes them very useful in disaster struck areas. They can enter into spaces which is otherwise difficult for humans to do so. Our plan is to propose such a system which can detect any life form stuck in such a danger zone and move it out of the space by performing coordinated actions of movements.
Location selection factors of concentrated solar power plant investments

This research aims to find, define, identify, describe, select and cluster (group, set) the location selection factors of very large concentrated solar power plant investments in Supergrids (super grid) and Global Grid with location selection factors module of the 1st generation Original Anatolian Honeybees' investment decision support methodology. 11 preliminary screening investment stage criteria (e.g. CA: Direct normal irradiation/irradiance/insolation (DNI), CD: High-Voltage Direct Current (HVDC) & High-Voltage Alternating Current (HVAC) electrification/power grid infrastructure, CG: Political, war, terror & security conditions), and 287 pre-feasibility investment stage criteria (e.g. CB: Governments' Supergrid integration policy, CB1: Common Supergrid framework, legislation, and directive development willingness, CB2: Common Supergrid electricity transmission law/regulation development willingness) are presented in a semantically clustered form by Grey Weighted Product Method based on expert judgments (3 linguistic variables in grey numbers and equal weight mean whitenization) weighting integrated with experience curve in select awarded criteria honeycomb cell, and Fuzzy Decision-Making Trial and Evaluation Laboratory combined with Simple Additive Weighting Method based on expert judgments (3 linguistic variables in triangular fuzzy membership functions, and graded mean integration defuzzification) weighting integrated with experience curve in complex clustering honeycomb cell. 11 criteria at only 1 hierarchical level (factors) and 287 criteria at 4 hierarchical levels (i.e. main factors, factors, 1st level sub-factor, 2nd level sub-factors) are semantically clustered respectively for preliminary screening and pre-feasibility project investment development stages.
Demonstrating Broadcast Aggregate Keys for Data Sharing in Cloud

In recent years, the major issue in cloud computing is providing privacy for getting to re-appropriating information put away on cloud. To store also to share information safely, cryptosystem is utilized. In cryptosystem, the client needs to scramble the data before securing data on cloud and after that decode the data to get to it. This errand can need numerous keys for information encode just like information decoding. But the problem arises with data integrity where data can be modified or the files can be replaced without the knowledge of the data owner. So, to ensure the trustworthiness of the record, the information proprietor figures the hash-based message authentication code (HMAC) signature on each encoded document.
Accuracy Booster: Performance Boosting using Feature Map Re-calibration

Convolution Neural Networks (CNN) have been extremely successful in solving intensive computer vision tasks. The convolutional filters used in CNNs have played a major role in this success, by extracting useful features from the inputs. Recently researchers have tried to boost the performance of CNNs by re-calibrating the feature maps produced by these filters, e.g., Squeeze-and-Excitation Networks (SENets). These approaches have achieved better performance by Exciting up the important channels or feature maps while diminishing the rest. However, in the process, architectural complexity has increased. We propose an architectural block that introduces much lower complexity than the existing methods of CNN performance boosting while performing significantly better than them. We carry out experiments on the CIFAR, ImageNet and MS-COCO datasets, and show that the proposed block can challenge the state-of-the-art results. Our method boosts the ResNet-50 architecture to perform comparably to the ResNet-152 architecture, which is a three times deeper network, on classification. We also show experimentally that our method is not limited to classification but also generalizes well to other tasks such as object detection.
Exploring Ethereum’s Blockchain Anonymity Using Smart Contract Code Attribution

Blockchain users are identified by addresses (public keys), which cannot be easily linked back to them without out-of-network information. This provides pseudo-anonymity, which is amplified when the user generates a new address for each transaction. Since all transaction history is visible to all users in public blockchains, finding affiliation between related addresses can hurt pseudo-anonymity. Such affiliation information can be used to discriminate against addresses that were found to be related to a specific group, or can even lead to the de-anonymization of all addresses in the associated group, if out-of-network information is available on a few addresses in that group. In this work we propose to leverage a stylometry approach on Ethereum's deployed smart contracts' bytecode and high level source code, which is publicly available by third party platforms. We explore the extent to which a deployed smart contract's source code can contribute to the affiliation of addresses. To address this, we prepare a dataset of real-world Ethereum smart contracts data, which we make publicly available; design and implement feature selection, extraction techniques, data refinement heuristics, and examine their effect on attribution accuracy. We further use these techniques to test the classification of real-world scammers data.
What settlements leave behind — pXRF compositional data analysis of archaeological layers from Tell el-Fara'in (Buto, Egypt) using machine learning

Abstract Modern portable and handheld XRF devices (pXRF) allow quick measurement of large geochemical datasets without the necessity for laboratory facilities. Such facilities are rare in Egypt and modern dating techniques which are indispensable in Geoarchaeology to establish a robust chronology are not available, as sample transport is restricted and Egypt does not provide OSL or AMS 14C laboratories. With these preconditions, we evaluate the usability of pXRF geochemical data for the dating of archaeological sediments with machine learning techniques. The sample material was collected via sampling of archaeological sections and profile walls from archaeological excavations in the northwestern Nile delta at the settlement site Buto (Tell el-Fara'in) and Kom el-Gir. Additionally, samples were taken from sediments and cultural layers uncovered from their surroundings using vibracore corings with open steel auger heads. In this methodological approach, we examine the applicability of pXRF methods and test the sample data for distinct geochemical differences between the main settlement phases with multivariate statistical methods. The dating is based on the training of artificial neural networks with pXRF data from archaeological material of well-dated context to date test data of cultural layers within the vibracores. This allows us to link fundamental changes in the landscape with the settlement history of Buto and neighboring tells.
Data Security in Cloud Computing Using Three-Factor Authentication

Cloud storage is tremendously increasing its services, huge range of storage and communication of massive data over network. This practically has an adverse effect on the way of upholding this data, when it especially comes to the privacy of the user-secured and highly confidential data. We first view you with a system that is vulnerable to this authentication protocol with its misuse of biometrics and incorrect password generates no user to lost the mobile device. We even went along with this scheme and gave out a major issue to overcome with impersonation attack. However, this scheme makes the way easy to attack for offline password guessing attack. We included a three-factor authentication which carries the smart card into card reader, gets the password and identity and conveys the user details' requesting time. We then came up with a scheme to overcome these security flaws of this prescribed authentication scheme combining passwords, mobile devices and biometrics. The proposed system is robust three-factor authentication with the help of password, biometrics and mobile device which provides reliable security strength to the user's data and makes counterattack to existing attack, giving with more benefits compared to the previous scheme. This scheme will not only encounters with the security issues, but also provides with most enhanced security functionalities.
Temporal Approaches for Human Activity Recognition Using Inertial Sensors

Human Activity Recognition (HAR) involves classifying one person's activity based on sensor data. In this work, inertial data, collected mainly from wearable sensors, are used to recognize HAR by using Convolutional Neural Networks (CNN) to extract features from the raw sensor data. Additionally, Temporal Convolutional Networks (TCN) are applied to classify the extracted features, comparing the overall performance of those layers with Long Short-Term Memory (LSTM) recurrent neural network layers. Several experiments are performed and the results show that TCN based architectures are able to outperform LSTM based architectures in sequence modeling.
TradeMap: A FINMA-compliant Anonymous Management of an End-2-end Trading Market Place

Data leaks and privacy scandals have been a growing concern of the last decade. While most traditional, i.e., centralized, online platforms require users to register with their personal data, they potentially expose the user's identity and data to be used for unintended purposes. This work proposes TradeMap as an integrated architecture, designing and enabling an online end-to-end (e2e) trading market place, while supporting anonymous management features. TradeMap addresses the Swiss Financial Market Supervisory Authority (FINMA) regulations by designing a FINMA-complaint Know Your Customer (KYC) platform. Additionally, TradeMap is based on blockchains and employs Ethereum Smart Contracts (SC). Thus, trust and anonymity between the market place and the KYC system relies on zero knowledge proof-based SCs used for user identification processes. With this management approach proposed, the user authentication is only verified within the KYC platform, providing a legally valid and fully anonymous online trading platform.
PABLO

As dynamically-typed languages grow in popularity, especially among beginning programmers, there is an increased need to pinpoint their defects. Localization for novice bugs can be ambiguous: not all locations formally implicated are equally useful for beginners. We propose a scalable fault localization approach for dynamic languages that is helpful for debugging and generalizes to handle a wide variety of errors commonly faced by novice programmers. We base our approach on a combination of static, dynamic, and contextual features, guided by machine learning. We evaluate on over 980,000 diverse real user interactions across four years from the popular PythonTutor.com website, which is used both in classes and by non-traditional learners. We find that our approach is scalable, general, and quite accurate: up to 77% of these historical novice users would have been helped by our top-three responses, compared to 45% for the default interpreter. We also conducted a human study: participants preferred our approach to the baseline ($p = 0.018)$, and found it additionally useful for bugs meriting multiple edits.
Stable Intrinsic Auto-Calibration from Fundamental Matrices of Devices with Uncorrelated Camera Parameters

Auto-Calibration is an important task in computer vision and is necessary for many visual applications. Methods like photogrammetry, depth map estimation, metrology, augmented/mixed reality or odometry are strongly dependent on well calibrated devices. While classical calibration relies on tools like checkerboards or additional scene information, auto-calibration only takes epipolar relations into account. Classical calibration is often impractical, tends to de-adjust over time and distributes the error over the entire, limited working volume. Auto-calibration, on the other hand, does not require any information other than the image content itself, has a virtually unlimited working range and usually achieves highest accuracy at the objects' surfaces. Unfortunately, auto-calibration methods are sensitive to errors in the fundamental matrix and need good initialization to converge to the global solution. In practice this leads to difficulties if optical parameters like principal point or focal length are unconstrained. In such situations, even state-of- the-art auto-calibration methods tend to diverge and do not yield a valid calibration. This work assesses reasons for this behavior, in particular for the initialization method of Bougnoux [3] and Lourakis' state-of-the-art auto-calibration method [21]. Based on the analysis, a more stable method is proposed. A continuous and smooth energy functional is introduced, providing superior convergence properties. I.e. it can not diverge, converges faster, and has a significantly enlarged convergence region with respect to the global minimum. Finally, a thorough evaluation has been conducted and a detailed comparison with the state of the art is presented.
Personalized Meal Planning for Diabetic Patients Using a Multi-Criteria Decision- Making Approach

Following a healthy diet is essential for people with diabetes. For this purpose, there have been many digital tools and mobile apps developed for diabetes meal planning. Most of them focus on controlling the blood sugar level of users. However, they undervalued the social, cultural, and religious significance of food to people. There are numerous factors that affect a person's meal planning including taste, nutrition, budget, preference, habit, and health constraints. People can struggle to decide what to eat. They may easily be overwhelmed by different food options and various constraints. In this paper, we propose a personalized meal planning strategy to support diabetes management. We develop a novel hybrid Multi- Criteria Decision-Making scheme for meal planning. Our goal is to effectively plan affordable and culturally appropriate meals to get all the nutrition needed for diabetic patients while still being mindful of calories and carbs.
Parallel Signal Processing Based-On Graphics Processing Units

This article analyzes parallel implementation of GPU in the process of generating speech spectrogram images, which is most commonly used as a parameter in speech recognition. The convenience of generating and recognizing spectrogram images from speech signals is that they are rich in character sets compared to other speech recognition methods. Therefore, most modern speech recognition systems use spectrograms. Based on the results of the experiment, it can be seen that the production of spectrogram images from speech signals in the graphics processor was faster than the central processor. This will improve the quality of speech recognition and identification in real time.
Multi-Modal Association based Grouping for Form Structure Extraction

Document structure extraction has been a widely researched area for decades. Recent work in this direction has been deep learning-based, mostly focusing on extracting structure using fully convolution NN through semantic segmentation. In this work, we present a novel multi-modal approach for form structure extraction. Given simple elements such as textruns and widgets, we extract higher-order structures such as TextBlocks, Text Fields, Choice Fields, and Choice Groups, which are essential for information collection in forms. To achieve this, we obtain a local image patch around each low-level element (reference) by identifying candidate elements closest to it. We process textual and spatial representation of candidates sequentially through a BiLSTM to obtain context-aware representations and fuse them with image patch features obtained by processing it through a CNN. Subsequently, the sequential decoder takes this fused feature vector to predict the association type between reference and candidates. These predicted associations are utilized to determine larger structures through connected components analysis. Experimental results show the effectiveness of our approach achieving a recall of 90.29%, 73.80%, 83.12%, and 52.72% for the above structures, respectively, outperforming semantic segmentation baselines significantly. We show the efficacy of our method through ablations, comparing it against using individual modalities. We also introduce our new rich human-annotated Forms Dataset.
Comments on “A Public Auditing Protocol With Novel Dynamic Structure for Cloud Data”

In this paper, we discuss a security weakness of Shen <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> ’s public auditing protocol for cloud data [IEEE Transactions on Information Forensics and Security, 12(10): 2402-2415, 2017.]. Specifically, we point out their protocol is vulnerable to a data privacy breach attack, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , an adversary, once he compromises the third-party auditor latently, can also obtain all data owners’ outsourced data by constructing appropriate challenges. As a result, it breaks the property of “privacy preserving”. We hope that by identifying this design flaw, similar weaknesses can be avoided in future designs.
Optimized deep neural network for cryptanalysis of DES

Cryptography is the study of techniques which used to transforms the original text (plain text) to cipher text (non understandable text). Due to recent progress on digitized data exchange in electronic way, information security has become crucial in data storage and transmission. Some of the crypto graphic algorithm has provided a promising solution which not only protects the data but also authenticates the systems and its participants, so the threat of various attacks is minimized. Nonetheless in the advancement of computing resources the cryptanalysis techniques also emerged and performing competitively in the field of information security with good results. In this paper, we have proposed the optimized deep neural network approach for cryptanalysis of symmetric encryption algorithm 64-bit DES (Data encryption standard). Our approach has used back propagation technique with multiple hidden layers and advanced activation function also we have addressed the problem of vanishing gradient. Further, the implementation results show that we have achieved 90% accuracy which is significantly higher as compared to previous approaches. We have also compared the proposed technique with the existing ones against three parameters i.e. time, loss, accuracy.