Reviewerly/exHarmony huggingface dataset:
When post-quantum cryptography meets the internet of things

Adapting to Post-Quantum Cryptography (PQC) is an inevitable shift due to stable quantum computers getting closer to fruition. As our lives are getting more entangled with Internet of Things (IoT) technologies, a quantum computer attack will be even more devastating. In this work, we evaluate the performance of PQC algorithms on IoT devices. Our empirical results show that PQC algorithms, besides their tolerance against quantum computer attacks, have the potential to be on par with typical asymmetric encryption algorithms from the TLS protocol.
Imperceptible black-box waveform-level adversarial attack towards automatic speaker recognition

Abstract Automatic speaker recognition is an important biometric authentication approach with emerging applications. However, recent research has shown its vulnerability on adversarial attacks. In this paper, we propose a new type of adversarial examples by generating imperceptible adversarial samples for targeted attacks on black-box systems of automatic speaker recognition. Waveform samples are created directly by solving an optimization problem with waveform inputs and outputs, which is more realistic in real-life scenario. Inspired by auditory masking , a regularization term adapting to the energy of speech waveform is proposed for generating imperceptible adversarial perturbations. The optimization problems are subsequently solved by differential evolution algorithm in a black-box manner which does not require any knowledge on the inner configuration of the recognition systems. Experiments conducted on commonly used data sets, LibriSpeech and VoxCeleb, show that the proposed methods have successfully performed targeted attacks on state-of-the-art speaker recognition systems while being imperceptible to human listeners. Given the high SNR and PESQ scores of the yielded adversarial samples, the proposed methods deteriorate less on the quality of the original signals than several recently proposed methods, which justifies the imperceptibility of adversarial samples.
Online astroturfing: A problem beyond disinformation

Coordinated inauthentic behaviours online are becoming a more serious problem throughout the world. One common type of manipulative behaviour is astroturfing. It happens when an entity artificially creates an impression of widespread support for a product, policy, or concept, when in reality only limited support exists. Online astroturfing is often considered to be just like any other coordinated inauthentic behaviour; with considerable discussion focusing on how it aggravates the spread of fake news and disinformation. This paper shows that astroturfing creates additional problems for social media platforms and the online environment in general. The practice of astroturfing exploits our natural tendency to conform to what the crowd does; and because of the importance of conformity in our decision-making process, the negative consequences brought about by astroturfing can be much more far-reaching and alarming than just the spread of disinformation.
Collision avoidance approaches for autonomous mobile robots to tackle the problem of pedestrians roaming on campus road

A novel algorithm for collision-free navigation of a mobile robot on the campus road with pedestrian roaming is described in this paper. Proposed method uses the RGB-D depth sensor, utilizing optical flow estimation and object detection, to predict pedestrian locations. Given these environmental uncertainties, we present a Velocity Obstacle (VO) algorithm based on mobility rules to calculate the velocity has been presented. It is proposed to use the Markov Decision Process (MDP) for decision-making (to maneuver the robot whenever it approaches the target). The proposed algorithm is a hybrid combination of deep learning and model-based techniques and provides better results in terms of navigation time and collision avoidance success rate than conventional algorithms. The real-time performance of the proposed algorithm is highlighted using a real-world dynamic scenario for an up-to-date kid ride car that has been redesigned to work as an autonomous mobile robot.
Detection of Leukemia using Machine Learning

Leukemia is a type of cancer which damages the blood-forming tissues that produces rbc's, wbc's and blood platelets, which is present inside bone marrow. Leukemia is the cancer that mainly occurs within blood forming tissues in the human body. It may slowly affect all the organs inside the body without any external indication leading to several other disorders. The diagnosis of leukemia in early stages of the disease is difficult as the current technologies are more time cons uming. Studies suggest that India ranks 3rd highest in reported cases of Blood Cancer, after USA and China. If a person is already diagnosed with any one among other types of cancer, then the chances of developing leukemia is nearly 30%. The symptoms of leukemia are tiredness, flu, fatigue and weight loss which makes it very difficult to predict manually by clinical methods. Since it is malignant and fatal it is neces s ary that it mus t be predicted earlier in order to undergo sufficient treatment and thereby saving millions of lives. As compared to all other types cancers, leukemia cases have been increasing dramatically in the entire world. Since the symptoms of leukemia are being very common, people fail to detect it in the early stage. The main objective of our project is to diagnose leukemia in the primary stage by analysing peripheral blood smear images and thus treatment could be given in specific and successful way. Thus our project focusses on using the best machine learning algorithms for detecting leukemia in a very early stage. This res earch study collects peripheral blood smear images (PBS), which are microscopic images of the blood samples. The images collected are preprocessed and then segmentation is performed where the images are segmented based on pixels and enlarged portion of affected area of the blood s ample image is us ed for feature extraction process. Finally classification is done using CNN in order to detect leukemia.
Fake Currency Recognition System Using Edge Detection

In this paper, we propose a system for currency recognition system and the detection of fake Indian currency banknotes using image processing techniques. It is hard for people to perceive monetary forms from various nations. Our point is to help individuals with taking care of this issue. In any case, money acknowledgement frameworks that are in light of picture investigation are entirely not adequate. Our framework depends on picture handling and makes the procedure programmed and vigorous. Our aim to assist those folks that are not ready to recognize which country&#x0027;s currency note was. We use banknotes which are currency, may differ the size, texture, color. Our system helps in Indian currency identification, which is a fake currency or not. In India,&#x2019; currency&#x2019; is Transaction, so there is more value for currency in our social and economic development. We have used MATLAB software to recognize other country currencies and Indian fake currency. Modernization within the money-related framework ensures financial improvement, and nowadays, the Indian government has become cognizant about this. Hence, Rs 1000 and Rs 500 notes&#x2019; demonetization is the foremost up-to-date case of it. However, we have Rs 2000 as another benefit showcase. In light of the top elevated worth note, quite possibly degenerate individuals will try to make it a fake. The real target of this project is to contemplate distinctive key highlights of recent certifiable money and utilize such systems to acknowledge and confirm new cash circled by India&#x0027;s depository institution. There are different strategies which are utilized to acknowledge fake notes and certifiable one. By utilizing various parts of Digital Image handling, such as picture preparing, Image Segmentation, feature extraction, and viewing pictures, we will then remove the highlights of certified notes. It is a problematic errand for recognizing counterfeit money.
A Computer-Assisted Preoperative Path Planning Method for the Parallel Orthopedic Robot

Background: Trajectory planning is the premise of the control of orthopedic robots, which is directly related to the safety of the human body. However, to date, the trajectory of orthopedic robots has been restricted to lines and spline curves. This limits the flexibility of the robot and leads to unsatisfactory performance. In this paper, a trajectory planning method based on improved RRT* and B-spline curve is proposed in order to improve the control accuracy and flexibility. Method: Firstly, combined with the shortcomings of current trajectory planning methods and bone docking task analysis, the characteristics of the trajectory for orthopedic robot are illustrated, and the problem is described. Secondly, a sampling strategy and an extension strategy are proposed to solve the optimal problem of the RRT* algorithm. Meanwhile, B-spline curve is selected for path smoothing. Thirdly, based on our orthopedic robot, kinematics analysis is introduced briefly, and hypotonic polynomial is used to fit the joint variables. Finally, a comparative study of the improved RRT*, RRT*, and other algorithms are completed, and the feasibility of the robot’s trajectory is verified by algorithm simulation and platform simulation. Results: Compared with RRT*, shorter path and high node utilization are shown in the improved RRT*, which cut down about 1mm in the average path length and increased about half in the average node utilization. In the meantime, the fitting results are accepted, and the results of algorithm simulation and platform simulation showed good consistency and feasibility. Conclusions: This study revealed that the improved RRT* was superior to RRT*, and the proposed method could be used for the trajectory planning of parallel orthopedic robots, which has some significance for bone fracture and deformity correction.
Accurate and efficient salient object detection via position prior attention

The excessive pursuit of accuracy has resulted in complex and huge structures of most existing salient object detection (SOD) models, and the meticulously designed lightweight SOD models cannot accurately detect salient objects. To improve the practicality of SOD, we design a novel position prior attention network (PPANet) for fast and accurate salient object detection in this paper. In detail, we propose a position prior attention module (PPAM), which first assigns different weights to positions based on the prior that objects near the image center are more attractive to people, and then perceives object context information through different receptive fields. In addition, we propose a context fusion module (CFM) to prevent the coarse resolution of high-level features from diluting the salient object boundaries during fusion. We present two PPANet versions: a heavyweight PPANet-R aimed at high accuracy SOD and a lightweight PPANet-M that achieves a good balance between accuracy and efficiency. Besides, we construct a structural polishing loss that gives more attention to object boundary and solves the problem of sample imbalance. Experimental results on 5 popular benchmark datasets demonstrate that the proposed PPANet-R outperforms existing SOD models, and PPANet-M achieves accuracy comparable to the state-of-the-art heavyweight SOD methods with 150 FPS real-time detection speed.
On a reparameterization of a flexible family of cure models

The existence of items not susceptible to the event of interest is of both theoretical and practical importance. Although researchers may provide, for example, biological, medical, or sociological evidence for the presence of such items (cured), statistical models performing well under the existence or not of a cured proportion, frequently offer a necessary flexibility. This work introduces a new reparameterization of a flexible family of cure models, which not only includes among its special cases, the most studied cure models (such as the mixture, bounded cumulative hazard, and negative binomial cure model) but also classical survival models (ie, without cured items). One of the main properties of the proposed family, apart from its computationally tractable closed form, is that the case of zero cured proportion is not found at the boundary of the parameter space, as it typically happens to other families. A simulation study examines the (finite) performance of the suggested methodology, focusing to the estimation through EM algorithm and model discrimination, by the aid of the likelihood ratio test and Akaike information criterion; for illustrative purposes, analysis of two real life datasets (on recidivism and cutaneous melanoma) is also carried out.
CE-Fed: Communication efficient multi-party computation enabled federated learning

Federated learning (FL) allows a number of parties collectively train models without revealing private datasets. There is a possibility of extracting personal or confidential data from the shared models even-though sharing of raw data is prevented by federated learning. Secure Multi Party Computation (MPC) is leveraged to aggregate the locally-trained models in a privacy preserving manner. However, it results in high communication cost and poor scalability in a decentralized environment. We design a novel communication-efficient MPC enabled federated learning called CE-Fed. In particular, the proposed CE-Fed is a hierarchical mechanism which forms model aggregation committee with a small number of members and aggregates the global model only among committee members, instead of all participants. We develop a prototype and demonstrate the effectiveness of our mechanism with different datasets. Our proposed CE-Fed achieves high accuracy, communication efficiency and scalability without compromising privacy.
Design and Evaluation of AR-Assisted End-User Robot Path Planning Strategies

Nowadays, robots play an increasingly important role but still usually have to be programmed by highly skilled professionals. Therefore, end-user solutions supporting users in solving (simple) robot programming tasks without expert knowledge are a promising research field. A possibility for these solutions is the inclusion of Augmented Reality (AR) to enable users to work in the robot space, reducing the amount of mentally taxing coordinate space conversions. Approaches for this mainly rely on the waypoint-based robot path programming strategy. To explore an alternative solution, we propose an AR-assisted approach with different possible path-planning strategies, such as drawing paths or selecting single waypoints directly in the real world. This enables end-users without a programming background to program paths for a wheeled mobile robot. They can also see and edit their programmed paths in a Blockly-like representation. Furthermore, we offer AR in-place program simulation and direct building of finished programs to the real robot. We evaluated our approach regarding usability compared to existing non-AR end-user software as well as comparing the two different path-planning strategies to each other. The evaluation showed that our approach is more usable and faster than the conventional method, while the differences between the path-planning methods are more nuanced and show qualities in both versions.
Markedness in Visual Semantic AI

We evaluate the state-of-the-art multimodal "visual semantic" model CLIP ("Contrastive Language Image Pretraining") for biases related to the marking of age, gender, and race or ethnicity. Given the option to label an image as "a photo of a person" or to select a label denoting race or ethnicity, CLIP chooses the "person" label 47.9% of the time for White individuals, compared with 5.0% or less for individuals who are Black, East Asian, Southeast Asian, Indian, or Latino or Hispanic. The model is also more likely to rank the unmarked "person" label higher than labels denoting gender for Male individuals (26.7% of the time) vs. Female individuals (15.2% of the time). Age also affects whether an individual is marked by the model: Female individuals under the age of 20 are more likely than Male individuals to be marked with a gender label, but less likely to be marked with an age label, while Female individuals over the age of 40 are more likely to be marked based on age than Male individuals. We trace our results back to the CLIP embedding space by examining the self-similarity (mean pairwise cosine similarity) for each social group, where higher self-similarity denotes greater attention directed by CLIP to the shared characteristics (i.e., age, race, or gender) of the social group. The results indicate that, as age increases, the self-similarity of representations of Female individuals increases at a higher rate than for Male individuals, with the disparity most pronounced at the "more than 70" age range. Six of the ten least self-similar social groups are individuals who are White and Male, while all ten of the most self-similar social groups are individuals under the age of 10 or over the age of 70, and six of the ten are Female individuals. Our results yield evidence that bias in CLIP is intersectional: existing biases of self-similarity and markedness between Male and Female gender groups are further exacerbated when the groups compared are individuals who are White and Male and individuals who are Black and Female. CLIP is an English-language model trained on internet content gathered based on a query list generated from an American website (Wikipedia), and results indicate that CLIP reflects the biases of the language and society which produced this training data.
ICSA: Intelligent chatbot security assistant using Text-CNN and multi-phase real-time defense against SNS phishing attacks

SNS phishing attack is one of the representative social engineering attacks exploiting humans’ emotions and trust and thus usually proceeds in multi-phases such that the attacker first forms an intimate bond with victims emotionally and then forces victims to conduct serious actions according to the attacker’s malicious intents. Meanwhile, according to our extensive survey, we observed that existing works on defending against SNS phishing attacks are inefficient in that they detect the attack mostly in the final stage of the attack or after the incident is reported. This is mainly because they neglect the characteristics of SNS phishing attacks following social engineering attack cycles. By this motivation, we first formalize SNS phishing attack phases based on the existing social engineering attack cycles and propose an Intelligent Chatbot Security Assistant (ICSA) that detects the progress phase of an SNS phishing attack by using Text-CNN-based attack phase classifiers and AI Chatbot technology. ICSA provides appropriate suggestions to victims and conducts necessary actions according to pre-defined defense produces designed by security experts. In addition, we implement ICSA as Telegram Chatbot by using Google Dialogflow and AWS (Amazon Web Services) server in the Telegram messenger. To validate our idea, we conduct extensive experiments to show our Telegram Chatbot works properly in real-time according to its design purpose and also we compare two representative ML models (Text-CNN and LSTM) in terms of the training and test accuracy to show why we choose the Text-CNN model to generate attack phase classifiers.
Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages

Abusive language is a growing concern in many social media platforms. Repeated exposure to abusive speech has created physiological effects on the target users. Thus, the problem of abusive language should be addressed in all forms for online peace and safety. While extensive research exists in abusive speech detection, most studies focus on English. Recently, many smearing incidents have occurred in India, which provoked diverse forms of abusive speech in online space in various languages based on the geographic location. Therefore it is essential to deal with such malicious content. In this paper, to bridge the gap, we demonstrate a large-scale analysis of multilingual abusive speech in Indic languages. We examine different interlingual transfer mechanisms and observe the performance of various multilingual models for abusive speech detection for eight different Indic languages. We also experiment to show how robust these models are on adversarial attacks. Finally, we conduct an in-depth error analysis by looking into the models’ misclassified posts across various settings. We have made our code and models public for other researchers1.
Multi-view stereo for large-scale scene reconstruction with MRF-based depth inference

PatchMatch-based multi-view stereo (MVS) algorithms have achieved great success in large-scale scene reconstruction tasks. However, reconstruction of textureless planes often fails, as similarity measurement methods may become ineffective in these regions. Thus, a potential depth hypothesis inference strategy is proposed to make the reconstruction result more complete. The strategy consists of two steps. First, for pixels that are not successfully reconstructed, multiple potential depth hypotheses are generated using neighboring reconstructed accurate depth values. Second, depth hypotheses are selected using the Markov random field (MRF). The strategy can significantly increase the reconstruction completeness. In addition, a new acceleration scheme similar to dilated convolution is proposed to speed up the depth map estimation process when using larger matching windows. We integrated the above ideas into a new MVS pipeline, potential hypothesis inference multi-view stereo (PHI-MVS). The result of PHI-MVS is validated on ETH3D and BlendedMVS benchmarks, and it demonstrates competing performance against the state-of-the-art.
A novel blockchain-based and proxy-oriented public audit scheme for low performance terminal devices

With the rapid development of cloud computing technology, more and more individuals/organizations are inclined to store their data in cloud server (CS). Cloud storage audit schemes can help data owner (DO) to confirm the integrity of their data, but the existing schemes still have some limitations. On one hand, the existing schemes assume that DO's terminal devices are computationally powerful enough to handle various operations in a timely manner. While in practice, the terminal devices probably are mobile phones, tablets and other devices with low computing power. On the other hand, the existing schemes rely on a fully trusted third-party auditor (TPA), but it is not in line with real application scenarios. Therefore, in this paper, we proposed a novel blockchain-based and proxy-oriented public audit (BBPO-PA) scheme for low performance terminal devices. Firstly, we introduced a trusted proxy authorized by DO, which can process and upload DO's encrypted files. Secondly, we applied blockchain in our scheme and utilized smart contracts instead of untrusted TPA to improve the reliability and stability of audit results. Thirdly, we took advantage of an index table to ensure that our scheme can support dynamic data operation. Finally, security analysis revealed that our scheme is provably secure in random oracle model. Meanwhile, performance analysis demonstrated that our scheme is efficient and especially suitable for low performance terminal devices.