Reviewerly/exHarmony huggingface dataset:
Managing Software Security Risks through an Integrated Computational Method

Security risk evaluation of web-based healthcare applications is important from a design perspective. The developers as well as the users need to make sure that the applications must be secure. Citing the disastrous effects of unsecured web applications, <i>Accuntix Online</i> states that the IT industry has lost millions of dollars due to security theft and malware attacks. Protecting the integrity of patients' health data is of utmost importance. Thus, assessing the security risk of web-based healthcare applications should be accorded the highest priority while developing the web applications. To fulfill the security requirements, the developers must meticulously follow the Multi-Criteria Decision-Making (MCDM) methodology in the assortment of the most effective procedure for security assessment right from the developmental phase of the application. To address the security-related issues in web-based healthcare applications, we have followed the fuzzy-based integrated technique to assess the security risk of web-based healthcare applications. Further, the integrated technology is the combination of Analytic Hierarchy Process (F-AHP) and Technique for Order of Preference by Similarity to Ideal Solution (F-TOPSIS) techniques. The F-AHP approach gives the weights of the factors. We classified the risks into two-levels, Level one had the safety measures factors such as access control, integrity, confidentiality, and, authentication. We selected ten different web-based healthcare applications as alternatives. The calculations based on the proposed methodology ranked the <i>pattern system for access control</i> to be the most prioritized attribute. The outcomes of the study and the procedure used in this assessment would support future research and specialists' initiatives in organizing web applications through advanced supportable safety and security.
Lightweight Single Image Super-resolution with Dense Connection Distillation Network

Single image super-resolution attempts to reconstruct a high-resolution (HR) image from its corresponding low-resolution (LR) image, which has been a research hotspot in computer vision and image processing for decades. To improve the accuracy of super-resolution images, many works adopt very deep networks to model the translation from LR to HR, resulting in memory and computation consumption. In this article, we design a lightweight dense connection distillation network by combining the feature fusion units and dense connection distillation blocks (DCDB) that include selective cascading and dense distillation components. The dense connections are used between and within the distillation block, which can provide rich information for image reconstruction by fusing shallow and deep features. In each DCDB, the dense distillation module concatenates the remaining feature maps of all previous layers to extract useful information, the selected features are then assessed by the proposed layer contrast-aware channel attention mechanism, and finally the cascade module aggregates the features. The distillation mechanism helps to reduce training parameters and improve training efficiency, and the layer contrast-aware channel attention further improves the performance of model. The quality and quantity experimental results on several benchmark datasets show the proposed method performs better tradeoff in term of accuracy and efficiency.
Federated Online Learning to Rank with Evolution Strategies: A Reproducibility Study

Online Learning to Rank (OLTR) optimizes ranking models using implicit users' feedback, such as clicks, directly manipulating search engine results in production. This process requires OLTR methods to collect user queries and clicks; current methods are not suited to situations in which users want to maintain their privacy, i.e. not sharing data, queries and clicks. Recently, the federated OLTR with evolution strategies (FOLtR-ES) method has been proposed to provide a solution that can meet a number of users' privacy requirements. Specifically, this method exploits the federated learning framework and $$\epsilon $$ -local differential privacy. However, the original research study that introduced this method only evaluated it on a small Learning to Rank (LTR) dataset and with no conformity with respect to current OLTR evaluation practice. It further did not explore specific parameters of the method, such as the number of clients involved in the federated learning process, and did not compare FOLtR-ES with the current state-of-the-art OLTR method. This paper aims to remedy to this gap. Our findings question whether FOLtR-ES is a mature method that can be considered in practice: its effectiveness largely varies across datasets, click types, ranker types and settings. Its performance is also far from that of current state-of-the-art OLTR, questioning whether the maintained of privacy guaranteed by FOLtR-ES is not achieved by seriously undermining search effectiveness and user experience.
Volumetric Occupancy Mapping With Probabilistic Depth Completion for Robotic Navigation

In robotic applications, a key requirement for safe and efficient motion planning is the ability to map obstacle-free space in unknown, cluttered 3D environments. However, commodity-grade RGB-D cameras commonly used for sensing fail to register valid depth values on shiny, glossy, bright, or distant surfaces, leading to missing data in the map. To address this issue, we propose a framework leveraging probabilistic depth completion as an additional input for spatial mapping. We introduce a deep learning architecture providing uncertainty estimates for the depth completion of RGB-D images. Our pipeline exploits the inferred missing depth values and depth uncertainty to complement raw depth images and improve the speed and quality of free space mapping. Evaluations on synthetic data show that our approach maps significantly more correct free space with relatively low error when compared against using raw data alone in different indoor environments; thereby producing more complete maps that can be directly used for robotic navigation tasks. The performance of our framework is validated using real-world data.
Experimental study of how far blood spatter stains on fabrics can be found from the blood source, and relevance to crime scene reconstruction

For investigative purposes, the bloodstain pattern analyst might have to estimate whether a given stain on fabric could have originated from a specific location. A wide range of values of the maximum distance that a blood drop can travel have been reported based on experiments, ranging from less than 1 m to more than 10 m. It is also known that stains on porous materials such as fabrics are more difficult to interpret than stains on non-absorbing surfaces, because of wicking. Here, we perform several fluid dynamic spatter experiments and formulate a fluid dynamics model to describe the trajectories of the blood drops. The experiments are performed with swine blood, the properties of which are well understood. The main parameters screened are the drop size, initial velocity, the launch angle, and the orientation of the fabric. A large number of blood drops are produced by impact events. The resulting stains on knitted white T-shirt fabric are digitally measured. Their position relative to the source and size is reported. Trajectories are simulated accounting for the influence of gravity and drag forces. A simple relation between drop size and stain size is established based on extensive experiments on a specific fabric. Results of the trajectory simulations are then searched and mined for parameters directly measurable on a crime scene, such as the stain size on fabric and the relative location of the fabric with respect to the blood source. The experimental results are compared and found in agreement with the numerical predictions. The results are presented in one chart relevant to crime scene reconstruction. The chart is easy to use and only requires minimum knowledge of fluid dynamics.
SyReNN: A Tool for Analyzing Deep Neural Networks

Abstract Deep Neural Networks (DNNs) are rapidly gaining popularity in a variety of important domains. Formally, DNNs are complicated vector-valued functions which come in a variety of sizes and applications. Unfortunately, modern DNNs have been shown to be vulnerable to a variety of attacks and buggy behavior. This has motivated recent work in formally analyzing the properties of such DNNs. This paper introduces SyReNN, a tool for understanding and analyzing a DNN by computing its symbolic representation. The key insight is to decompose the DNN into linear functions. Our tool is designed for analyses using low-dimensional subsets of the input space, a unique design point in the space of DNN analysis tools. We describe the tool and the underlying theory, then evaluate its use and performance on three case studies: computing Integrated Gradients, visualizing a DNN’s decision boundaries, and patching a DNN.
Investigation of Noise Effects for Different Quantum Computing Architectures in IBM-Q at NISQ Level

Today, all the implemented quantum computers are in Noisy Intermediate-Scale Quantum (NISQ) level. In such quantum computers, when circuit length and size increase, the results become less reliable because of the increasing effect of noise. The noise is an important factor that should be handled in NISQ level quantum computers. In this study, we investigate the noise factor on 5 qubit IBM-Q computers for basic circuits. For this purpose, existing 5 qubit IBM-Q computers with different architectures are examined. Then quantum circuit equivalents corresponding to basic logic gates such as XOR, AND, and OR are presented. Quantum circuits created for XOR, AND, and OR are run 10 times on different quantum computers of 5 qubits named Santiago, Athens, Valencia, Vigo, Ourance, and IBMQX2. Statistical information such as std., mean is obtained from the results. The consistency of the results obtained and their difference from the optimum result are discussed. As a result of the study, there is no contradiction in the results obtained with quantum volume. Studies show that even small growths in XOR, AND, and OR circuits can lead to a big deflection in results.
Machine learning based Diagnosis and Classification Of Sickle Cell Anemia in Human RBC

Anemia is a disease which is caused by the deficiency of red blood cells. The shape of red blood cell changes to sickle or crescent shape in sickle cell anemia disease. The manual inspection of microscopic images is very difficult and time-consuming process. In this research image processing and machine learning techniques is used to automate the process of detection of sickle cells in microscopic images then classify the RBC into three shapes: circular, elongated (sickle cell) and other shape. The microscopic image is pre-processed and Otsu thresholding technique is used for segmentation. Then, Watershed segmentation is applied to separate the overlapped cells. The geometrical, statistical and textural features are extracted from the images. The machine learning classifier random forest, logistic regression naïve baye sand support vector machine is used. This research describes the comparison among these algorithms.
Trick the body trick the mind: avatar representation affects the perception of available action possibilities in virtual reality

In immersive Virtual Reality (VR), your brain can trick you into believing that your virtual hands are your real hands. Manipulating the representation of the body, namely the avatar, is a potentially powerful tool for the design of innovative interactive systems in VR. In this study, we investigated interactive behavior in VR by using the methods of experimental psychology. Objects with handles are known to potentiate the afforded action. Participants tend to respond faster when the handle is on the same side as the responding hand in bi-manual speed response tasks. In the first experiment, we successfully replicated this affordance effect in a Virtual Reality (VR) setting. In the second experiment, we showed that the affordance effect was influenced by the avatar, which was manipulated by two different hand types: (1) hand models with full finger tracking that are able to grasp objects, and (2) capsule-shaped—fingerless—hand models that are not able to grasp objects. We found that less than 5 mins of adaptation to an avatar, significantly altered the affordance perception. Counter intuitively, action planning was significantly shorter with the hand model that is not able to grasp. Possibly, fewer action possibilities provided an advantage in processing time. The presence of a handle speeded up the initiation of the hand movement but slowed down the action completion because of ongoing action planning. The results were examined from a multidisciplinary perspective and the design implications for VR applications were discussed.
Graph-Based Methods to Detect Hate Speech Diffusion on Twitter

In this paper, we investigate models to detect the spread of hate speech on Twitter based on its diffusion in the network graph. We experiment with a dataset of 10,000 tweets manually labelled as hate speech or not and show that classification based solely on the sharing graph yields strong F1 scores for our task and high hate speech detection precision. We also highlight the vulnerability of existing textual hate speech detection methods to adversarial attacks and demonstrate that while our methods do not outperform state-of-the-art text models, graph-based models provide robust detection mechanisms and are able to detect instances of hate speech that fool text classifiers. We find that graph convolutional networks produce the strongest hate speech F1 score of 0.58 and find other success with kernel methods. Finally, we also consider the effects of automated bots in the sharing of hate speech content and find they are insignificant in our experiments.
Toxic Comment Classification Implementing CNN Combining Word Embedding Technique

With the advancement of technology, the virtual world and social media have become an important part of people's everyday lives. Social media allows people to connect, share their emotions and discuss various subjects, yet it also becomes a place or cyberbullying, personal attack, online harassment, verbal abusing and other kinds of toxic comments. Top social media platform still suffering from fast and accurate classification to remove this kind of toxic comment automatically. In this paper, an ensemble methodology of convolution neural networking (CNN) and natural language processing (NLP) is proposed which segments toxic and non-toxic comments in first phase, and then it classifies and labels in six types based on the dataset of Wikipedia's talk page edits, collected from Kaggle. The proposed architecture is structured following data preprocessing applying data cleaning processes, adopting NLP techniques like tokenization, stemming and converted word into vector by word embedding techniques. Ensembling the preprocessed dataset and best word embedded method, CNN model is applied that scores ROC-AUC 98.46 and 98.05% accuracy for toxic comment classification which is higher than compared existing works.
Streaming Attention-Based Models with Augmented Memory for End-To-End Speech Recognition

Attention-based models have been gaining popularity recently for their strong performance demonstrated in fields such as machine translation [1] and automatic speech recognition [2]. One major challenge of attention-based models is the need of access to the full sequence and the quadratically growing computational cost concerning the sequence length. These characteristics pose challenges, especially for low-latency scenarios, where the system is often required to be streaming. In this paper, we build a compact and streaming speech recognition system on top of the end-to-end neural transducer architecture [3] with attention-based modules augmented with convolution [2]. The proposed system equips the end-to-end models with the streaming capability and reduces the large footprint from the streaming attention-based model using augmented memory [4], [5]. On the LibriSpeech [6] dataset, our proposed system achieves word error rates 2.7% on test-clean and 5.8% on test-other, to our best knowledge the lowest among streaming approaches reported so far.
Virtual fitting room technology in fashion design

Abstract The presence of technology is inevitable is several fields, including in fashion design. This paper identifies technology-based virtual fitting rooms. All types of technology-based virtual rooms were identified; offline, online, web-based, mobile-based. It has been found that some of the most commonly used virtual rooms in fashion design involves such technologies as 2D fashion design, 3D fashion design, digital communication, e-commerce, internet-based, ICT-based, e-magazines, and e-books. It has also been inferred that virtual fitting rooms are starting to change conventional ones.
Entropy based C4.5-SHO algorithm with information gain optimization in data mining

Information efficiency is gaining more importance in the development as well as application sectors of information technology. Data mining is a computer-assisted process of massive data investigation that extracts meaningful information from the datasets. The mined information is used in decision-making to understand the behavior of each attribute. Therefore, a new classification algorithm is introduced in this paper to improve information management. The classical C4.5 decision tree approach is combined with the Selfish Herd Optimization (SHO) algorithm to tune the gain of given datasets. The optimal weights for the information gain will be updated based on SHO. Further, the dataset is partitioned into two classes based on quadratic entropy calculation and information gain. Decision tree gain optimization is the main aim of our proposed C4.5-SHO method. The robustness of the proposed method is evaluated on various datasets and compared with classifiers, such as ID3 and CART. The accuracy and area under the receiver operating characteristic curve parameters are estimated and compared with existing algorithms like ant colony optimization, particle swarm optimization and cuckoo search.
Differential Contour Stellar-Based Radio Frequency Fingerprint Identification for Internet of Things

Data attacks from illegal access devices of the Internet of Things will cause serious interference and threats to the entire network.It is difficult to ensure the security of the communication system only by relying on traditional application layer password authentication methods.Therefore, it is of great significance to design an effective physical layer authentication system based on radio frequency fingerprints.Regarding the issue above, this paper proposes a novel physical layer authentication method for Internet of Things based on differential contour stellar.Through the test of identification and authentication of 20 WiFi network card devices from same manufacturer, same type and same batch, the recognition accuracy rate can reach 98.6% by the proposed method.The proposed method can improve the effect of radio frequency fingerprint identification from three aspects: i.The differential processing can effectively reduce the negative influence of phase rotation caused by carrier frequency offset and Doppler effects; ii.The color processing can effectively reduce the negative influence of random noise caused by channel noise; iii.It is suitable for processing large-scale networks and the massive data they bring.
Real-Time Decision Making for a Car Manufacturing Process Using Deep Reinforcement Learning

Computer simulations of manufacturing processes are in widespread use for optimizing production planning and order processing. If unforeseeable events are common, real-time decisions are necessary to maximize the performance of the manufacturing process. Pre-trained AI-based decision support offers promising opportunities for such time-critical production processes. Here, we explore the effectiveness of deep reinforcement learning for real-time decision making in a car manufacturing process. We combine a simulation model of a central production part, the line buffer, with deep reinforcement learning algorithms, in particular with deep Q-Learning and Monte Carlo tree search. We simulate two different versions of the buffer, a single-agent and a multi-agent one, to generate large amounts of data and train neural networks to represent near-optimal strategies. Our results show that deep reinforcement learning performs extremely well and the resulting strategies provide near-optimal decisions in real-time, while alternative approaches are either slow or give strategies of poor quality.
On the Gap between Domestic Robotic Applications and Computational Intelligence

Aspired to build intelligent agents that can assist humans in daily life, researchers and engineers, both from academia and industry, have kept advancing the state-of-the-art in domestic robotics. With the rapid advancement of both hardware (e.g., high performance computing, smaller and cheaper sensors) and software (e.g., deep learning techniques and computational intelligence technologies), robotic products have become available to ordinary household users. For instance, domestic robots have assisted humans in various daily life scenarios to provide: (1) physical assistance such as floor vacuuming; (2) social assistance such as chatting; and (3) education and cognitive assistance such as offering partnerships. Crucial to the success of domestic robots is their ability to understand and carry out designated tasks from human users via natural and intuitive human-like interactions, because ordinary users usually have no expertise in robotics. To investigate whether and to what extent existing domestic robots can participate in intuitive and natural interactions, we survey existing domestic robots in terms of their interaction ability, and discuss the state-of-the-art research on multi-modal human–machine interaction from various domains, including natural language processing and multi-modal dialogue systems. We relate domestic robot application scenarios with state-of-the-art computational techniques of human–machine interaction, and discuss promising future directions towards building more reliable, capable and human-like domestic robots.