Reviewerly/exHarmony huggingface dataset:
An improved Simhash algorithm based malicious mirror website detection method

Abstract There are a large number of similar or even identical webpages on the Internet. These webpages will cause unnecessary loss of network resources, including waste of storage space, decreased web search speed, and decreased user experience. And some malicious mirror websites will become tools for criminals to carry out illegal activities such as phishing attacks. In this paper, the autours analyzed the mainstream text similarity detection algorithms and webpage deduplication algorithms, and proposed an improved webpage deduplication algorithm based on Simhash. The algorithm converts the text collection into Simhash fingerprints for storage through mapping, and calculates the similarity of the two fingerprints through Hamming distance, thereby obtaining the similarity of the webpage. Experiments show that the algorithm proposed in this paper has a higher accuracy rate and recall rate, and can be better applied to the identification and detection of malicious mirror websites.
A Weakly-Supervised Named Entity Recognition Machine Learning Approach for Emergency Medical Services Clinical Audit

Clinical performance audits are routinely performed in Emergency Medical Services (EMS) to ensure adherence to treatment protocols, to identify individual areas of weakness for remediation, and to discover systemic deficiencies to guide the development of the training syllabus. At present, these audits are performed by manual chart review, which is time-consuming and laborious. In this paper, we report a weakly-supervised machine learning approach to train a named entity recognition model that can be used for automatic EMS clinical audits. The dataset used in this study contained 58,898 unlabeled ambulance incidents encountered by the Singapore Civil Defence Force from 1st April 2019 to 30th June 2019. With only 5% labeled data, we successfully trained three different models to perform the NER task, achieving F1 scores of around 0.981 under entity type matching evaluation and around 0.976 under strict evaluation. The BiLSTM-CRF model was 1~2 orders of magnitude lighter and faster than our BERT-based models. Our proposed proof-of-concept approach may improve the efficiency of clinical audits and can also help with EMS database research. Further external validation of this approach is needed.
A Data Middle Platform Architecture Based on Microservice Serving Power Grid Business

At present, the general capacity building based on the data middle platform is still in the exploratory stage, the enterprise information service capacity has not been effectively accumulated, and the phenomenon of repeated development is serious. This paper proposes a data middle platform architecture based on microservices, which can precipitate enterprise-level data resources and model service capabilities, avoid repeated development of the same functional code, repeated model training, and shortens the model iteration cycle and development cost. The architecture can currently support the accumulation and reuse of general data, general feature operators, and general machine learning models, and can efficiently support file transfer between business information systems and data middle platform.
Various Image Modalities Used in Computer-Aided Diagnosis System for Detection of Breast Cancer Using Machine Learning Techniques: A Systematic Review

The detection of breast cancer using medical images popularly known as histopathology images often leads to a confusion and disagreement among pathologist. The most invasive cancer in females: breast cancer is affecting 12% for population worldwide. Breast cancer has no physical symptoms unless there is sign of painless lump. Breast cancer pathology images are classified into: normal, benign and malignant. Breast cancer cell has sub-classes interrelated to cells’ variability, organization and density with the structure of tissues and morphology. Main purpose of this systematic review is to investigate the performances of breast cancers diagnosis system using machine learning algorithm to find out the actual position of cancerous tissues in human body. We cover a different accentuation on the convolutional neural network (CNN) strategy for breast cancer image detection. Alongside with CNN various machine learning techniques, SVM, KNN, logistic regression and random forest are being used for breast cancer detection. Eventually, various studies were systematically reviewed, and the related articles published from 2010 to 2020 were considered. Result is calculated based on performance metric elements: accuracy, sensitivity, F1-score and area under curve. Best accuracy achieved so far is 98.9% using CNN classifier. This paper demonstrates various techniques as well as results of systematic review of breast cancer detection using CAD systems. Current statistics shows that different machine learning algorithm helps to improve detection and still requires more improvement and more flexibility to detect the breast cancer more accurately.
History Reuse and Bag-of-Words Loss for Long Summary Generation

Recurrent Neural Network (RNN) based abstractive text summarization models have made great progress over the past few years, largely triggered by the encoder-decoder architecture. However, there has been little work improving the generation of relatively long summaries. In this paper, we concentrate on two prominent problems in long summary generation. First, although significant efforts have been made to assist the encoder in handling long sequences, the decoder struggles with long sequences owing to the limited storage capacity of RNN. We propose a simple and effective approach called history reuse, which first mines critical information from the history summary sequence and then transmits the information to the decoder. Second, since encoder-decoder models are typically trained to produce exactly the same summary as the target summary, certain word order deviations between the predicted summary and target summary are excessively punished. Accordingly, we introduce a fully differentiable loss called bag-of-words (BoW) loss, which takes advantage of the feature of BoW discarding word order information in texts, and computes the difference between the two summaries at the BoW space. Experiments on two benchmark datasets, CNN/Daily Mail and Pubmed, demonstrate that our methods significantly improve the baseline.
A Study on Intervention of Chatbots in Recruitment

The greatest dare across the HR industry today across the globe is that they all have bulk numbers of resumes in their application tracking system (ATS) and customer relation management (CRM) systems, but filtering those resumes and mapping candidates to job openings from these systems manually plays a very tiresome task for recruiters. Finding qualified candidates from the applicant pool and fitting them in the right place is the key to successful recruiting. Technological innovations though help recruiters on a day-to-day basis, bringing out the most effective and efficient strategy is still a question. This is possible with the help of technology advancement in the form of artificial intelligence (AI) which could play a key role in making the recruitment process more operative. AI takes several forms, but this paper focuses on the role and impact of chatbots in recruitment industry. This study was conducted among 120 IT/ITES recruiters in South India. This paper highlights on the impact of an artificial intelligence chatbot on recruitment process. The authors have used correlational analysis and multiple regression analysis to test the facilitated hypothesis. This paper augments that artificial intelligence chatbots are very effective techniques which need to be implemented in recruitment process as it automates the whole process which eases the job of a recruiter. This study also details on the usage of various chatbots across the globe and its benefits for the recruitment process to be effective and efficient for the organizations to sustain in the competitive environment.
Machine Learning Techniques for Improved Breast Cancer Detection and Prognosis—A Comparative Analysis

Breast cancer prevails as the most widespread and second deadliest cancer in women. The typical symptoms of breast cancer are minimal and curable when the tumor is small, hence screening is crucial for timely detection. Since delayed diagnosis contributes to considerable number of deaths, a number of cross-disciplinary techniques have been introduced in medical sciences to aid healthcare experts in the swift detection of breast cancer. A vast amount of data is collected in the process of breast cancer detection and therapy through consultation reports, histopathological images, blood test reports, mammography results etc. This data can generate highly powerful prediction models, if properly used that can serve as a support system to assist doctors in the early breast cancer diagnosis and prognosis. This paper discusses the need of machine learning for breast cancer detection, presents a systematic review of recent and notable works for precise detection of breast cancer, followed by a comparative analysis of the machine learning models covered in these studies. Then, we have performed breast cancer detection and prognosis on three benchmark datasets of Wisconsin, using seven popular machine learning techniques, and noted the findings. In our experimental setup, K-Nearest Neighbor and Random Forest perform with the highest accuracy of 97.14% over Wisconsin Breast Cancer Dataset (original). Moreover, random forest displays best performance over all the three datasets. Finally, the paper summarizes the challenges faced together with conclusions drawn and prospective scope of machine learning in breast cancer detection and prognosis.
Research on Block Chain Defense against Malicious Attack in Federated Learning

Federated learning enables participants to be capable of collaboratively building powerful machine learning models and exploiting privacy protection mechanisms to protect data privacy. However, the federal-learning security mechanism remains not perfect. Using malicious training samples is considered to be vulnerable to attacks against machine learning models. Lastly, the substitution technique of the model is adopted to eliminate the learning effect of the final model. Though numerous security protocols have been formulated to defend against and prevent malicious attacks, most are active defenses against malicious attacks, instead of passive ones (e.g., distillation-based defense and regularization-based adversarial training). In the present study, a block chain-based passive defense model is proposed to protect the model. To be specific, when a participant updates its local model, model participation, user fingerprints and other key information will be stored on the chain. Malicious attacks can be therefore traced and detected. To ensure data integrity and confidentiality, model data is encrypted and updated via the blockchain. Record rewards for active participation in federal learning and punish violators. Accordingly, the dynamic protection mechanism of federated learning is achieved.
High-Throughput Elliptic Curve Cryptography Using AVX2 Vector Instructions

Single Instruction Multiple Data (SIMD) execution engines like Intel’s Advanced Vector Extensions 2 (AVX2) offer a great potential to accelerate elliptic curve cryptography compared to implementations using only basic x64 instructions. All existing AVX2 implementations of scalar multiplication on e.g. Curve25519 (and alternative curves) are optimized for low latency. We argue in this paper that many real-world applications, such as server-side SSL/TLS handshake processing, would benefit more from throughput-optimized implementations than latency-optimized ones. To support this argument, we introduce a throughput-optimized AVX2 implementation of variable-base scalar multiplication on Curve25519 and fixed-base scalar multiplication on Ed25519. Both implementations perform four scalar multiplications in parallel, where each uses a 64-bit element of a 256-bit vector. The field arithmetic is based on a radix- $$2^{29}$$ representation of the field elements, which makes it possible to carry out four parallel multiplications modulo a multiple of $$p = 2^{255} - 19$$ in just 88 cycles on a Skylake CPU. Four variable-base scalar multiplications on Curve25519 require less than 250,000 Skylake cycles, which translates to a throughput of 32,318 scalar multiplications per second at a clock frequency of 2 GHz. For comparison, the to-date best latency-optimized AVX2 implementation has a throughput of some 21,000 scalar multiplications per second on the same Skylake CPU.
Single-shot spectrally resolved interferometry for the simultaneous measurement of the thickness and surface profile of multilayer films

We present a single-shot spectrally-resolved interferometry for simultaneously measuring the film thickness and surface profile of each layer of a patterned multilayer film structure. For this purpose, we implemented an achromatic phase shifting method based on the geometric phase using the polarization characteristics of the light and obtained four phase-shifted interferograms in the spectrally-resolved fringe pattern at the same time by combining a pixelated polarizing camera with an imaging spectrometer. As a result, we could simultaneously measure the reflectance and phase of the sample over a wide wavelength range with a single measurement. To evaluate the validity of the proposed method, we measured a patterned five-layer film specimen and compared our measurement results with those from commercial instruments, an ellipsometer and a stylus profiler, respectively. We confirmed the results matched each other well.
Whisper: A Curious Case of Valid and Employed Mallory in Cloud Computing

Apart from Security and privacy, there are crucial requirements of secrecy for modern practice in Cloud Computing. Secrecy protocol keeps client’s data secret, and it protects the secrets from being misused by any other unintended users. It is the essential requirement to establish trust among users, vendors, and countries. However, the most modern system does not provide secrecy of user and user’s data. Therefore, the secrecy of the user and the user’s data remains no more secret. It is exposed intentionally or unintentionally. Moreover, there is no data secrecy for administrators in Cloud Computing such that the administrators cannot easily misuse the data. Therefore, trust in the company needs to be restored by devising a new protocol. Hence, we propose a secrecy protocol for establishing trust among the users, vendors, and countries, called Whisper, which maintains hard secrecy protocol. Also, this secrecy protocol follows "only by my choice" philosophy. This paper demonstrates how to achieve this protocol to re-establish trust among users, vendors, and countries in Cloud Computing. Whisper uses asymmetric cryptography protocol, which requires random numbers and prime numbers. We have tested the randomness of generated pseudo-random numbers of Whisper in NIST SP 800-22 and passes all 15 statistical tests of randomness for various input. This paper also presents the generation of a randomly chosen prime number and a random number, and demonstrates how to reproduce the randomly chosen prime number and the generated random number to eliminate administrators from list of intended user’s list.
CyberSecurity in Healthcare Industry

Today healthcare system is considered one of the most vulnerable systems for cyber attackers. This is because healthcare system deals with highly sensitive data such as payment information, patient's history, patient's private information and so on. In terms of cyberattacks, healthcare systems result in significant damage to both organizations and patients. The goal of this study is to discuss why we need to increase the security in healthcare system and determine how to increase the security in healthcare infrastructure.
Key Factors in the Process of Acceptance and Implementation of Artificial Intelligence in the Hotel Sector

Artificial intelligence is one of the most innovative and trending technologies in the hotel sector. It is transforming the hotel sector into a novel environment in business-client relationships. For this reason, this study seeks to find, through a literary review and a causal study carried out by experts in technologies and hotels, using the methodology of fuzzy cognitive maps, what the determining factors in the decision of hotels to accept and implement artificial intelligence in their hotels and in their services are. The list obtained is made up of the following factors (in order of relevance in this decision): top management support, perceived reliability of the technology, security, relative advantage, support from information systems providers, technological organizational readiness, government pressure or incentives, compatibility, business partner pressure, customer pressure, complexity, pressure from competitors, perceived cost, characteristics of the leader or manager, and size of the company.
From “no clear winner” to an effective Explainable Artificial Intelligence process: An empirical journey

Abstract “In what circumstances would you want this AI to make decisions on your behalf?” We have been investigating how to enable a user of an Artificial Intelligence‐powered system to answer questions like this through a series of empirical studies, a group of which we summarize here. We began the series by (a) comparing four explanation configurations of saliency explanations and/or reward explanations. From this study we learned that, although some configurations had significant strengths, no one configuration was a clear “winner.” This result led us to hypothesize that one reason for the low success rates Explainable AI (XAI) research has in enabling users to create a coherent mental model is that the AI itself does not have a coherent model. This hypothesis led us to (b) build a model‐based agent, to compare explaining it with explaining a model‐free agent. Our results were encouraging, but we then realized that participants' cognitive energy was being sapped by having to create not only a mental model, but also a process by which to create that mental model. This realization led us to (c) create such a process (which we term After‐Action Review for AI or “AAR/AI”) for them, integrate it into the explanation environment, and compare participants' success with AAR/AI scaffolding vs without it. Our AAR/AI studies' results showed that AAR/AI participants were more effective assessing the AI than non‐AAR/AI participants, with significantly better precision and significantly better recall at finding the AI's reasoning flaws.
Feeding Three Birds With One Scone: A Generic Duplication Based Countermeasure To Fault Attacks

In the current world of the Internet-of-things and edge computing, computations are increasingly performed locally on small connected systems. As such, those devices are often vulnerable to adversarial physical access, enabling a plethora of physical attacks which is a challenge even if such devices are built for security. As cryptography is one of the cornerstones of secure communication among devices, the pertinence of fault attacks is becoming increasingly apparent in a setting where a device can be easily accessed in a physical manner. In particular, two recently proposed fault attacks, Statistical Ineffective Fault Attack (SIFA) and the Fault Template Attack (FTA) are shown to be formidable due to their capability to bypass the common duplication based countermeasures. Duplication based countermeasures, deployed to counter the Differential Fault Attack (DFA), work by duplicating the execution of the cipher followed by a comparison to sense the presence of any effective fault, followed by an appropriate recovery procedure. While a handful of countermeasures are proposed against SIFA, no such countermeasure is known to thwart FTA to date. In this work, we propose a novel countermeasure based on duplication, which can protect against both SIFA and FTA. The proposal is also lightweight with only a marginally additional cost over simple duplication based countermeasures. Our countermeasure further protects against all known variants of DFA, including Selmke, Heyszl, Sigl's attack from FDTC 2016. It does not inherently leak side-channel information and is easily adaptable for any symmetric key primitive. The validation of our countermeasure has been done through gate-level fault simulation.
Automatic Play Segmentation of Hockey Videos

Most team sports such as hockey involve periods of active play interleaved with breaks in play. When watching a game remotely, many fans would prefer an abbreviated game showing only periods of active play. Here we address the problem of identifying these periods in order to produce a time-compressed viewing experience. Our approach is based on a hidden Markov model of play state driven by deep visual and optional auditory cues. We find that our deep visual cues generalize well across different cameras and that auditory cues can improve performance but only if unsupervised methods are used to adapt emission distributions to domain shift across games. Our system achieves temporal compression rates of 20-50% at a recall of 96%.
Structure preservation adversarial network for visual domain adaptation

Domain adaptation has attracted attention by leveraging knowledge from well-labeled source data to facilitate unlabeled target learning tasks. Numerous research efforts have been devoted to extracting effective features by incorporating the pseudolabels of target data. However, the transferable knowledge reflected by intradomain structure, interdomain correlation and label supervision has scarcely been considered simultaneously. In this paper, we propose a novel structure preservation adversarial network with target reweighting (SPTR) for unsupervised domain adaptation, in which local structure consistencies and category-level semantic alignment are simultaneously considered in the adversarial learning framework. Based on the labeled and pseudolabeled samples, we attempt to align both global and category-level domain statistics from different domains and simultaneously enforce structural consistency from feature space to label space in the source and target domains. Furthermore, to suppress the influence of falsely labeled target samples, a novel and generalized sample reweighting strategy is developed to assign target samples with different levels of confidence, which fully explores the knowledge of the target distribution to benefit the semantic transfer process. The experimental results in three transfer learning scenarios demonstrate the superiority of our proposed method over other state-of-the-art domain adaptation algorithms.