Reviewerly/exHarmony huggingface dataset:
Border Basis Computation with Gradient-Weighted Normalization

Normalization of polynomials plays a vital role in the approximate basis computation of vanishing ideals. Coefficient normalization, which normalizes a polynomial with its coefficient norm, is the most common method in computer algebra. This study proposes the gradient-weighted normalization method for the approximate border basis computation of vanishing ideals, inspired by recent developments in machine learning. The data-dependent nature of gradient-weighted normalization leads to better stability against perturbation and consistency in the scaling of input points, which cannot be attained by coefficient normalization. Only a subtle change is needed to introduce gradient normalization in the existing algorithms with coefficient normalization. The analysis of algorithms still works with a small modification, and the order of magnitude of time complexity of algorithms remains unchanged. We also prove that, with coefficient normalization, which does not provide the scaling consistency property, scaling of points (e.g., as a preprocessing) can cause an approximate basis computation to fail. This study is the first to theoretically highlight the crucial effect of scaling in approximate basis computation and presents the utility of data-dependent normalization.
Real-Time Object Detection System with Voice Feedback for the Blind People

Lots of people suffer from temporary or permanent disabilities, and one of them is blindness or the visually impaired. According to the World Health Organization (WHO), more than 1 billion people suffer from blindness. Technology may assist blind and visually impaired persons in a number of ways; objects detecting is still a difficult process. There are so many techniques available for object detection. But, the accuracy and efficiency of detection aren’t good. Therefore, in this paper, authenticate object detection in real time using the YOLO-v3 and deep learning techniques. The main focus of this paper is to create a smartphone application that is cost-efficient with high accuracy.
Automatically Generated Feature Model from Requirements: Toward an Enhanced Formalism

The feature model is one of the most important concepts in software product line development process, as it helps to represent commonalities and variabilities among software products. Several research works are currently conducted to generate a feature model of a software product line from its requirements document. However, the generated feature models do not include data and actors aspects which are essential parts of any software product line. The work presented in this paper is part of an automated feature model generating project which explores suitable input requirements document notations, simplifies the generating process, and outputs generated feature model formalism. It aims to propose a powerful feature model that should be the target of any automated process generating a feature model. Thus, in this work, different recent techniques that automatically generate feature models are analyzed. The lack of data and actors in these generated feature models was stated, and a feature model, aimed to be a good generated target, integrating data and actors with other conventional features is proposed. A case study evaluating the proposed target feature model formalism is presented.
Age as a Determinant in Selecting Type of Breast Cancer Surgery in Lovely Pink Solo Cancer Community

Breast-Conserving Surgery as a treatment option for older patients with early breast cancerHanum Faeni, MD1., Kristanto Yuli Yarso, MD2., Brian Wasita, MD3., Rachmi Fauziah Rahayu, MD4., Suyatmi Suyatmi, MD5., Nanang Wiyono, MD6., Riza Novierta Persik, MD3., Iman Hakim Wicaksana, MD1., Akhmad Azmiardi, MD7., Zhafira Ramadhanty, MD8.1 Resident of Surgery, Department of Surgery, Universitas Sebelas Maret, Surakarta, Jawa Tengah, Indonesia2 Oncology Surgeon, Department of Surgery, Universitas Sebelas Maret, Surakarta, Jawa Tengah, Indonesia3 Department of Anatomical Pathology, Universitas Sebelas Maret, Surakarta, Jawa Tengah, Indonesia4 Department of Radiology, Universitas Sebelas Maret, Surakarta, Jawa Tengah, Indonesia5 Department of Histology, Universitas Sebelas Maret, Surakarta, Jawa Tengah, Indonesia6 Department of Anatomy, Universitas Sebelas Maret, Surakarta, Jawa Tengah, Indonesia7 Doctoral Program on Public Health, Universitas Sebelas Maret, Surakarta, Jawa Tengah, Indonesia8 Medical Student, Universitas Sebelas Maret, Surakarta, Jawa Tengah, Indonesia&#x0D; Objective: Breast cancer is one of the most common malignancies in Indonesia. Breast cancer occurs due to rapid and abnormal cell growth due to infiltration of lymphatic tissue and blood vessels. For this reason, surgery is vital to use as a treatment for breast cancer at an early stage. There are two surgical methods: Breast-Conserving Surgery (BCS) and mastectomy. In this regard, several factors have been studied to influence patients in choosing BCS or mastectomy. Looking at the age factor, the results varied and differed significantly throughout the study.Method: This analytical study used a retrospective cross-sectional approach. The research subjects were patients with breast cancer who were the Lovely Pink community members in Surakarta City, Central Java. Subjects were selected by simple random sampling with inclusion criteria consisting of female patients aged 20 to 80 years, being diagnosed with stage 1 and 2 breast cancer, and having received surgery employing BCS or mastectomy techniques.Result: This study was conducted on 218 breast cancer patients who had undergone mastectomy or BCS in Surakarta City, Central Java. It was found that 104 patients were &lt; 50 years old and 114 patients &gt; 50 years old. 76 patients (34.9%) and 142 patients (65.1%) had a total income of more or less than Rp2.5 million/month. As many as 155 patients (71.1%) and 63 patients (28.9%) had no history of disease. Based on the type of surgery performed, 141 people (64.7%) underwent mastectomy, and 77 people (35.3%) underwent BCS.Conclusion: It can be concluded that patients prefer to undergo mastectomy with significant results at the age of under and above 50 years supported by income, medical history, and work history.Keywords: breast-conserving surgery, mastectomy, age
An image-based decoupling controller of quadrotor for moving target tracking

Different from the traditional controller system for quadrotor tasks, the vision-based strategies are more practical and powerful to execute more complex tasks, becoming more attractive to researchers. In this paper, an image-feature-based controller with states extracted in images directly is proposed for the quadrotor to track a moving underground target, in which suitable image features are defined and a coupling problem between position and attitude loop is solved by a decoupling algorithm. Moreover, the external disturbances caused by visual noise, wind, or other problems are eliminated by a robust observer with low-pass filters. A Lyapunov-based stability method is presented to prove the convergent properties of the system. Finally, a simulation using python3.6 with realistic images is established to verify that the system is stable and with high performance, the results of which show the data of unmanned aerial vehicle in moving target tracking.
A Novel Computer Vision Model for Recognition and Evaluation of Mathematical Equations using Deep Learning Technique

Most classes have been shifted to an online mode of education including primary education. Teachers can be in charge of multiple students and having to evaluate the answer sheets of these many students can be hard after a long day of work. Our system aims to automate this evaluation process by taking into consideration all the current practices followed by teachers and students so that our solution can be optimal for use as soon as possible. We have taken into consideration the use of apps that scan answer sheets. We aim to create a system that can take these scanned images, evaluate them and output a CSV sheet with the grades assigned to each student so the faculty can quickly go through it. For ease of understanding, the system will also output the images with the correct and wrong answers highlighted accordingly.
Precise Key Frames Adversarial Attack against Deep Reinforcement Learning

Due to the extensive development of deep neural networks, such as strategy based neural networks, they are easy to be deceived and fooled, resulting in model failure or wrong decision. Because DRL has made great achievements in various complex tasks, it is essential to design effective attacks to build a robust DRL algorithm. So far, most of them are to separate the model from the environment and select effective disturbances through several input and output attempts to achieve the purpose of attack. Therefore, this paper proposes a way to predict the future critical state time and attack by observing each state of the environment without constantly observing the input and output of the model. It is verified in Atari game, which can effectively reduce the acquisition of cumulative rewards on the premise of high efficiency and concealment. This method is suitable for most application scenarios, and ensures the characteristics of efficient and covert attack.
Accurate Recognition of Motion Patterns Based on Artificial Visual Neural Network

In order to improve the accuracy of motion pattern recognition, this paper combines the artificial visual neural network to construct a motion pattern recognition system. Moreover, this paper discusses the psychological perception properties of human eyes to color stimuli and gives a description of the observation field of view where the color stimuli are located. At the same time, this paper analyzes the phenomenon of color adaptation and provides a method of color appearance matching through modeling to achieve color appearance matching under variable observation conditions. Based on the chromatic adaptation transformation, a chromatic appearance model is given, which can predict the corresponding color and also predict the chromatic appearance properties of color stimuli under given observation conditions. In addition, this paper constructs an intelligent motion pattern recognition system combined with artificial visual neural network. The experimental results show that the motion pattern recognition system based on artificial visual neural network can accurately identify the motion pattern category.
A Novel Area Efficient $$\text {GF}(2^m)$$ Multiplier

Ali, Asfak Sarkar, Ram Das, Debesh KumarGalois field multiplication has received a lot of attention from researchers due to its use in encryption, channel coding, and digital signal processing. This paper proposes an area-efficient Galois field multiplier. A sequential approach is adopted here to implement the multiplier in field programmable gate arrays (FPGA). The proposed design gets a $$64.58 \%$$ improvement in an area with respect to 16-bit combinational implementation and $$48 \% $$ with respect to 16-bit sequential implementation. The proposed method also implemented using fixed polynomial, where the design gets a $$73.9 \%$$ improvement in an area with respect to 16-bit combinational implementation and $$55 \% $$ with respect to 16-bit sequential implementation. In comparison to early methods, the time delay is also decreased in most cases.
Functionally Interpreting Security

ABSTRACT The outcomes of an exploration of what security means as an engineered function are presented, based upon principles of secure system design. The exploration was done to support an effort to formalize the syntax and semantics for the expression of security protection needs in system requirements, and to enable alignments with system safety and resilience requirements. The exploration produced a clearer interpretation of the essential aspects of security as postulated by foundational work on secure system design and was cross checked against representative classes of security requirement criteria to confirm accuracy and sufficiency of coverage of key security requirement types. The resultant perspective of what it means to be secure can be useful to inform the development of effective practices for the design of secure systems that are aligned with Future of Systems Engineering (FuSE) concepts, particularly security as a functional requirement. Further, the perspective helps to distinguish those characteristics of security (loss of “anything”) that are in common, and that contrast, with safety (loss of “specific things”) and resilience (loss of “capability”).
Psychological efficiency of organizational technology of modular developmental interaction in the space of HEI

n the article modular-developmental interaction is considered as an integral attributive core of the defended innovative educational model, marked by the original organizational climate of the institution, by psychosocial space of parity educational cooperation, psycho-artistic technologies of real educational interaction and problem-dialogue techniques of a full-fledged educational process implementation. It appears as a scientifically designed, psycho-artistically embodied and organization-technologically carried out an innovative-psychological co-activity of participants in the educational process, aimed at obtaining, processing, transforming and self-creating of common tangible or intangible (process, state, image, etc.) object of cognition-construction according to personal tasks of vitacultural development of each participant as a subject, personality, individuality and universum. It is empirically argued that the innovative model of cyclical development of modular-developmental interaction between participants in the educational process of modern HEI (higher educational institution) is much more effective than the classical-academic system of education. In particular, the introduction of experimental variables in three universities of Ukraine (West Ukrainian National University, Odessa National University named after I.I. Mechnikov, Poltava National Pedagogical University named after V.G. Korolenko) made it possible to increase the overall effectiveness of lessons (trainings) (expert-psychological methodic by A.V. Furman), led to a statistically significant increase in intellectual, emotion-volitional, communicative indicators of experimental groups representatives (it had been diagnosed with 16-factor personality questionnaire by R. Kettel), caused constructive changes of typical models and strategies of personality interaction (it had been identified by the methodic of diagnosing interpersonal relationships by T. Liri, K. Thomas’ test “Types of behavior in a conflict”), and also provided the step-by-step development of academic groups as harmonized student groups (modified sociometry of J. Moreno). As a result, the idea of psychological determinants of the projective transition of domestic higher education from directive-extensive to psychologically intensive models of youth socialization on the basis of parity was significantly expanded and the form-content advantages of the system of modular-developmental learning by A.V. Furman were clarified, which provides a socio-culturally mediated space of continuous development-dialogical interaction between the subjects of the educational process.
JoTA: Aligning Multilingual Job Taxonomies through Word Embeddings (Student Abstract)

We propose JoTA (Job Taxonomy Alignment), a domain-independent, knowledge-poor method for automatic taxonomy alignment of lexical taxonomies via word embeddings. JoTA associates all the leaf terms of the origin taxonomy to one or many concepts in the destination one, employing a scoring function, which merges the score of a hierarchical method and the score of a classification task. JoTA is developed in the context of an EU Grant aiming at bridging the national taxonomies of EU countries towards the European Skills, Competences, Qualifications and Occupations taxonomy (ESCO) through AI. The method reaches a 0.8 accuracy on recommending top-5 occupations and a wMRR of 0.72.
Torsion point attacks on ‘SIDH‐like’ cryptosystems

Abstract Isogeny‐based cryptography is a promising approach for post‐quantum cryptography. The best‐known protocol following that approach is the supersingular isogeny Diffie–Hellman protocol (SIDH); this protocol was turned into the CCA‐secure key encapsulation mechanism SIKE, which was submitted to and remains in the third round of NIST's post‐quantum standardisation process as an ‘alternate’ candidate. Isogeny‐based cryptography generally relies on the conjectured hardness of computing an isogeny between two isogenous elliptic curves, and most cryptanalytic work referenced on SIKE's webpage exclusively focusses on that problem. Interestingly, the hardness of this problem is sufficient for neither SIDH nor SIKE. In particular, these protocols reveal additional information on the secret isogeny, in the form of images of specific torsion points through the isogeny. This paper surveys existing cryptanalysis approaches exploiting this often called ‘torsion point information’, summarises their current impact on SIKE and related algorithms, and suggests some research directions that might lead to further impact.
Importance of excitation source and sequence learning towards spoken language identification task

Spoken LID systems generally capture the long term temporal dynamic information present in the speech signal. To achieve that, sequence modeling techniques are used after the feature extraction process. But, the performance of the spoken LID system degrades in cross channel and noisy scenarios. From the literature, we can observe the benefit of excitation source information in noisy and cross-channel scenarios. Besides that, excitation features are also used as complementary evidence in spoken LID systems with spectral features. Motivated from this, an excitation based feature called integrated residual linear frequency cepstral coefficient (IRLFCC) has been proposed in this work. This work also provides a comparison between various deep learning based sequence modeling architectures towards capturing spoken language specific information. The experiments are performed using OLR2020 dataset. From the experiments, it can be observed that in the cross channel scenario, the proposed best system provides a relative improvement of 70.5% and 57.2% over the baseline in terms of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$EER_{avg}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$C_{avg}$</tex> respectively. Similarly, in the noisy scenario, the proposed best system provides a relative improvement of 37.8% and 45 % over the baseline system.
Few-Shot Segmentation via Capturing Interclass and Intraclass Cues Using Class Activation Map

Few-shot segmentation is a challenging task due to the limited class cues provided by a few of annotations. Discovering more class cues from known and unknown classes is the essential to few-shot segmentation. Existing method generates class cues mainly from common cues intra new classes where the similarity between support images and query images is measured to locate the foreground regions. However, the support images are not sufficient enough to measure the similarity since one or a few of support mask cannot describe the object of new class with large variations. In this paper, we capture the class cues by considering all images in the unknown classes, i.e., not only the support images but also the query images are used to capture the foreground regions. Moreover, the class-level labels in the known classes are also considered to capture the discriminative feature of new classes. The two aspects are achieved by class activation map which is used as attention map to improve the feature extraction. A new few-shot segmentation based on mask transferring and class activation map is proposed, and a new class activation map based on feature clustering is proposed to refine the class activation map. The proposed method is validated on Pascal Voc dataset. Experimental results demonstrate the effectiveness of the proposed method with larger mIoU values.
Linear Classification on Noisy Hardware

Motivated by the growing interest in machine learning on nanoscale edge devices, we study the effect of hardware noise and quantization errors on the accuracy of inference by linear classifiers. Our experiments on synthetic and real data sets using well accepted models for hardware noise and errors show that they have a significant impact on the accuracy. For mitigating those effects, we propose an easily implementable strategy by combining insights from linear classification, convex analysis and concentration of measure. Evaluations on synthetic and real data sets show that our simple strategy improves the performance significantly. We end with a brief discussion on a few avenues for further explorations.
Entropy Modeling via Gaussian Process Regression for Learned Image Compression

Existing entropy models in learned image compression are cumbersome to generate fixed mean and variance for estimating Gaussian distributions for latent representation. In this paper, we propose a novel entropy model based on Gaussian process regression (GPR) that flexibly predicts the mean of Gaussians with posterior distributions characterized by covari-ance functions spanned in the high-dimensional feature space. Furthermore, we develop the rate-distortion optimization based on the proposed entropy model by approximating the bitrates with the evidence lower bound (ELBO) derived via variational inference for GPR. The proposed model can be seamlessly integrated into existing end-to-end optimized frame-works by substituting the masked convolution based autoregressive models. Experimental results demonstrate that the proposed model outperforms conventional image compression methods such as JPEG2000 and BPG, as well as recent learning based methods on the Kodak dataset in terms of rate-distortion performance.
Contrastive Learning-Based Domain Adaptation for Semantic Segmentation

Semantic segmentation is a crucial algorithm for identifying various objects in the surrounding of an autonomous vehicle. However, due to the limited size of real-world datasets, domain adaptation is employed. Hence, the models are made to adapt to real-world settings while being trained on large-scale synthetic datasets. In domain adaptation, domain-invariant features play a significant role in learning domain agnostic representations for each predefined category. While most of the prior work focuses on decreasing the distance between the domains, the works that utilize contrastive objectives for learning domain-invariant features depend heavily on the augmentations used. In this work, we completely eradicate the requirement of explicit data augmentations. We hypothesize that real-world images and their corresponding synthetic images are different views of the same abstract representation. To enhance the quality of domain-invariant features, we increase the mutual information between the two inputs. We first validate our hypothesis on the classification task using the standard datasets; Office31 and VisDA-2017. Further, we perform quantitative and qualitative analysis on the segmentation task using SYNTHIA, GTA and Cityscapes datasets.