Reviewerly/exHarmony huggingface dataset:
General Obstacle Detection by Ground Shape Invariant Features with a Fisheye Camera

Reliable detection of obstacles around the vehicle is crucial for autonomous cars. We present a novel and robust ground shape invariant feature method for general obstacle detection with a car-mounted monocular fisheye camera. Both stationary and moving obstacles can be detected by our approach without recovering the camera motion. Firstly, In order to compute the ground shape invariant feature, the image is mapped into the top view image. And then feature points are extracted and matched between adjacent frames. Secondly, the points are grouped according to image patch partition. Finally, the ground shape invariant feature is computed for each group of points to detect obstacle points. Extensive experiments have been carried out with prerecorded video sequences including various obstacle types, various scenes and various illumination conditions. The experimental results show promising detection performance of the proposed method.
Career success as a socio-psychological condition for the realization of personality in professional activity

We focused attention on the socio-psychological aspect of a career, expressed in its targeted, procedural and effective components. Such a vision allows us to consider it as a purposeful process of active self-realization in professional activity on the basis of existing meta-skills and subjective awareness of career success. We also noted the connection between developed communication skills and a successful career. And on what values are central to the structure of the individual, its further path and goals, and then the strategy for their implementation, depend. In order to prove the last statement, we gave the results of an empirical study. According to the received data, the main life values that guide young specialists in building successful professional careers are safety, kindness, independence, achievement, universalism, conformism. Correlation analysis of distinguished values, career goals and strategies for their achievement made it possible to distinguish the typology of careers of young specialists at a university (stable career, team, unstable, independent, conformal, professional, active). We considered in detail factors both contributing and preventing successful construction of professional career, successful realization of personality in career. The identified theoretical aspects formed the basis for further empirical study of the goals, motives, values, strategies, factors and other socio-psychological features of the successful professional career of young specialists in various fields.
How Bad Are Bad Templates? Optimistic Design-Stage Side-Channel Security Evaluation and its Cost

Cryptographic designs are vulnerable to side-channel analysis attacks. Evaluating their security during design stages is of crucial importance. The latter is achieved by very expensive (slow) analog transient-noise simulations over advanced fabrication process technologies. The main challenge of such rigorous security-evaluation analysis lies in the fact that technologies are becoming more and more complex and the physical properties of manufactured devices vary significantly due to process variations. In turn, a detailed security evaluation process imposes exponential time complexity with the circuit-size, the number of physical implementation corners (statistical variations) and the accuracy of the circuit-simulator. Given these circumstances, what is the cost of not exhausting the entire implementation space? In terms of simulation-time complexity, the benefits would clearly be significant; however, we are interested in evaluating the security implications. This question can be formulated for many other interesting side-channel contexts such as for example, how would an attack-outcome vary when the adversary is building a leakage template over one device, i.e., one physical corner, and it performs an evaluation (attack) phase of a device drawn from a different statistical corner? Alternatively, is it safe to assume that a typical (average) corner would represent the worst case in terms of security evaluation or would it be advisable to perform a security evaluation over another specific view? Finally, how would the outcome vary concretely? We ran in-depth experiments to answer these questions in the hope of finding a nice tradeoff between simulation efforts and expertise, and security-evaluation degradation. We evaluate the results utilizing methodologies such as template-attacks with a clear distinction between profiling and attack-phase statistical views. This exemplary view of what an adversary might capture in these scenarios is followed by a more complete statistical evaluation analysis utilizing tools such as the Kullback–Leibler (KL) divergence and the Jensen-Shannon (JS) divergence to draw conclusions.
Use of popular electronic resources to improve the visibility of the course solving tasks in preparing students for USE in computer science

Abstract Currently, the main way to check the quality of education in schools in the Russian Federation is the state final certification, conducted in the format of a unified state exam. The unified state exam in computer science includes 27 tasks of various levels of complexity. Analysis of statistics on the success of various tasks shows that students experience the greatest difficulties when solving task number 23. To increase the visibility of the process of solving this task, you can use the well-known electronic resource MS Excel, which is part of the Microsoft MSOffice package. This electronic resource has a built-in programming language Visual Basic for Application, with which you can significantly expand the functionality of MS Excel. Written in Visual Basic for Application, the macro allows the student not only to get the correct answer, but also to view the problem solution tree, compare it with their own solution, and identify errors in their solution if there is a discrepancy. This article is devoted to the methodology of using this electronic resource in the process of preparing students for the unified state exam in computer science.
A Practice of the Chinese Stock Market-Based Medium-long-term Momentum Investment

The momentum stock-selection investment came into light with the broadening of the idea of quantum investment. The existing practice of momentum investment on the Chinese market mostly focuses on mid-short time intervals. The problem remains whether there is possibility of applying momentum strategy on a mid-long time interval, and if the strategy be profitable and risk-free, or at least, be steady. In this paper, we present the practice of momentum investment based on the history data of Chinese stock market by stretching the time interval to a mid-long term, and evaluate the profitability with back-testing over history data. We carried out our experiment by three steps: set the amount of holding stocks (which is 10 in our experiment), select the stock with the momentum strategy, and re-arrange our holding stocks after a certain time-frame. Our experiment showed that the new investment strategy has a profit rate of 134.83%, annualized rate of profit at 33.8% and its Sharpe ratio is at 0.785, which means that the mid-long term momentum strategy is appliable in the Chinese stock market, and it can be both profitable and steady.
Teaching technosphere safety and environmental culture at a technical university

The article discusses the psychological and pedagogical components of the environmental-cultural development of students of technical and humanitarian specialties, as well as cause-and-effect relationships of environmental problems of the largest industrial enterprises. The necessity of using control methods and modern technical means to ensure human security in the modern world is justified. The control effects on the elements of the technosphere space that support the biosphere and technosphere in a qualitative state are analyzed. The article reveals the prospects for common activities between the technical university and companies that are aimed at ensuring the necessary level of technosphere security.
Random Sample Consensus for the Robust Identification of Outliers in Cancer Data

Random sample consensus (Ransac) is a technique that has been widely used for modeling data with a large amount of noise. Although successfully employed in areas such as computer vision, extensive testing and applications to clinical data, particularly in oncology, are still lacking. We applied this technique to synthetic and biomedical datasets, publicly available at The Cancer Genome Atlas (TCGA) and the UC Irvine Machine Learning Repository, to identify outliers in the classification of tumor samples. The results obtained by combining Ransac with logistic regression were compared against a baseline classical logistic model. To evaluate the robustness of this method, the original datasets were then perturbed by generating noisy data and by artificially switching the labels. The flagged outlier observations were compared against the misclassifications of the baseline logistic model, along with the evaluation of the overall accuracy of both strategies. Ransac has shown high precision in classifying a subset of core (inlier) observations in the datasets evaluated, while simultaneously identifying the outlier observations, as well as robustness to increasingly perturbed data.
Para-sagittal Midclavicular Plane Localization in 3DCT Using Multi-Agent Dueling Network

Locating the para-sagittal on the left portal vein in the abdominal enhanced 3DCT data is a critical step in clinical diagnosis and multi-modality fusion. The process of manually annotating landmarks on the anatomical structure is timeconsuming, laborious, and requires a wealth of professional knowledge. In recent years, reinforcement learning (RL) has developed rapidly, making it possible to deal with complex medical data. This paper is based on model-free RL to train a joint multi-agent detection system. The sharing part of the network provides implicit communication for collaborative work between agents, and independent output helps each agent achieve the purpose of locating specific critical points, respectively. Results show that this method can identify three anatomical landmarks simultaneously on the para-sagittal on the left portal vein correctly, introducing a novel formulation for locating the standard plane of volumes and can deal with complex situations such as intraoperative image fusion. Also, the multi-model convergence time is 30% shorter than that of the single-agent model.
Joint Opinion Target and Target-oriented Opinion Words Extraction by BERT and IOT Model

In this paper, we investigate two sub-tasks of aspect-based sentiment analysis (ABSA) through the pre-trained language model BERT, namely opinion target extraction (OTE) and target-oriented opinion words extraction (TOWE). Specifically, we build a novel framework for the joint extraction model of opinion target and target-oriented opinion words feedback, which aims to extract the opinion target and corresponding opinion words. In order to accomplish the TOWE task more effectively, we proposed an IO-LSTM+Transformer structure, termed IOT, which has excellent performance in domain-specific datasets when combined with the BERT pre-training model. To validate the effectiveness of our model, we develop a pipeline model for comparison. Experiment results show that our model can extract the pair of opinion target and opinion words from the sentence more effectively than the pipeline model. Therefore, our joint model has the potential to facilitate other tasks of ABSA.
A Comparative Study in Israel and Slovenia Regarding the Awareness, Knowledge, and Behavior Regarding Cyber Security

With the COVID-19 pandemic, many organizations and institutions moved to e-learning and to e-working from home. With the increase in internet usage, the rate of cyber-attacks have also increased, and this was followed by the request for more cyber security behaviors from employees and students. In the current study, the authors explore the connection between cyber security awareness, cyber knowledge, and cyber security behavior. The authors measured the behaviors among students in two similar countries: Israel and Slovenia. Results show that students felt they had adequate awareness on cyber threat but apply only a few protective measures to protect their devices, usually relatively common and simple ones. The study findings also show that awareness to cyber threats mediate the connection between knowledge and protection behaviors, but only in the case that the knowledge is specific with regard to IT protection courses. Results, implications, and recommendations for effective cyber security training programs for organizations and academic institutions are presented and discussed.
INSPIRE: Intensity and spatial information-based deformable image registration

We present INSPIRE, a top-performing general-purpose method for deformable image registration. INSPIRE brings distance measures which combine intensity and spatial information into an elastic B-splines-based transformation model and incorporates an inverse inconsistency penalization supporting symmetric registration performance. We introduce several theoretical and algorithmic solutions which provide high computational efficiency and thereby applicability of the proposed framework in a wide range of real scenarios. We show that INSPIRE delivers highly accurate, as well as stable and robust registration results. We evaluate the method on a 2D dataset created from retinal images, characterized by presence of networks of thin structures. Here INSPIRE exhibits excellent performance, substantially outperforming the widely used reference methods. We also evaluate INSPIRE on the Fundus Image Registration Dataset (FIRE), which consists of 134 pairs of separately acquired retinal images. INSPIRE exhibits excellent performance on the FIRE dataset, substantially outperforming several domain-specific methods. We also evaluate the method on four benchmark datasets of 3D magnetic resonance images of brains, for a total of 2088 pairwise registrations. A comparison with 17 other state-of-the-art methods reveals that INSPIRE provides the best overall performance. Code is available at github.com/MIDA-group/inspire.
Games as Teaching Methods in Power Engineering Education

Modern federal state educational standards specify the requirements for the development of not only general professional and professional competencies for students but also general cultural competencies after completing a bachelor's program. In this regard, it is necessary to revise not only the established approaches to teaching but also the form of the final certification. It should be also considered that most students now perceive information from the outside world through their eyes, that is, they are visual learners. The paper presents the experience of the implementation of imitation methods of teaching in the disciplines of the educational program “Electric Power and Electrical Engineering" at Novosibirsk State Technical University (NSTU). Examples of intermediate attestation of students in an interactive form are presented: brainstorming, charades, defense of the work to an invited expert, etc. In addition, the process of transition from the classic oral exam to the Case exam is considered in detail. The authors share their experience and talk about the difficulties in introducing interactive teaching methods into the power engineering education.
How do Legislations, Regulations, Policies and Cases Influence the Security in E-Commerce?

As e-business becomes part of a majority of people's everyday life that appears to be risk-adverse, protection becomes crucially important (E-Commerce & Growth Study 2003). Internet security problems take several forms: spam, malware, site squatting, piracy, copyright infringement, denial of service, unauthorized intrusion into corporate or personal computers and networks (theft or misuse of the information stored therein), infringements of privacy, abuse and harassment. The following topics will be discussed for the purpose of this study: e-commerce laws, computer crimes, e-privacy and authentication & encryption. It is proposed that these three (3) principles are examined in order to tackle security and safety in e-commerce. These principles are derived from the review of all the legislations, cases, guidelines, policies and regulations in this paper. All these principles are complementary to each other. Self-regulation, Ethics, Enforcement. Security and safety will remain the most vital issues in electronic commerce that will continue to be discussed, examined and addressed. Actions will be continue to be taken by the society to create new laws, technology, methods and processes to increase security and safety in the cyberspace as e-commerce technologies grow. One must not be deterred from electronic commerce just because of these issues. There will always be a downside for everything including electronic commerce. Business growth from this new method of doing business is very lucrative to be abandoned.
Creativity and moral capacities

In the article the possibility of establishing a connection between the creative and moral spheres of the person is discussed. The author analyzes the results of foreign and domestic researches devoted to the study of the relationship between creativity and morality. The concept of "moral capacities" is proposed, its definition is given and three aspects of its study are identified. The results of a study of the relationship between creativity and the cognitive aspect of moral capacities in children of primary school age are presented. The empirical study involved 130 people; age – 7-9 years; boys – 71 people and girls – 59 people. The following methods were used: Torrance Test of Creative Thinking – figural form (study of the creative sphere) and two methods that assess children's ideas about moral behavior (study of the moral sphere). The results obtained show that there is no connection between the indicators of creativity and moral capacities in children of 7-9 years old, which is explained, according to the author, by their age characteristics. When interpreting the results, the specificity of primary school age is considered, associated with a turning point in the child's life.
A Hybrid Cache HW/SW Stack for Optimizing Neural Network Runtime, Power and Endurance

Hybrid caches consisting of both SRAM and emerging Non-Volatile Random Access Memory (eNVRAM) bitcells increase cache capacity and reduce power consumption by taking advantage of eNVRAM&#x0027;s small area footprint and low leakage energy. However, they also inherit eNVRAM&#x0027;s drawbacks, including long write latency and limited endurance. To mitigate these drawbacks, many works propose heuristic strategies to allocate memory blocks into SRAM or eNVRAM arrays at runtime based on block content or access pattern. In contrast, this work presents a HW/SW Stack for Hybrid Caches (SHyCache), consisting of a hybrid cache architecture and supporting programming model, reminiscent of those that enable GP-GPU acceleration, in which application variables can be allocated explicitly to the eNVRAM cache, eliminating the need for heuristics and reducing cache access time, power consumption, and area overhead while maintaining maximal cache utilization efficiency and ease of programming. SHyCache improves performance for applications such as neural networks, which contain large numbers of invariant weight values with high read/write access ratios that can be explicitly allocated to the eNVRAM array. We simulate SHyCache on the gem5-X architectural simulator and demonstrate its utility by benchmarking a range of cache hierarchy variations using three neural networks, namely, Inception v4, ResNet-50, and SqueezeNet 1.0. We demonstrate a design space that can be exploited to optimize performance, power consumption, or endurance, depending on the expected use case of the architecture, while demonstrating maximum performance gains of 1.7 /1.4/1.3x and power consumption reductions of 5.1/5.2/5.4x, for Inception/ResNet/SqueezeNet, respectively.
Learning over the Attentional Space with Mobile Robots

The advancement of technology has brought many benefits to robotics. Today, it is possible to have robots equipped with many sensors that collect different kinds of information on the environment all time. However, this brings a disadvantage: the increase of information that is received and needs to be processed. This computation is too expensive for robots and is very difficult when it has to be performed online and involves a learning process. Attention is a mechanism that can help us address the most critical data at every moment and is fundamental to improve learning. This paper discusses the importance of attention in the learning process by evaluating the possibility of learning over the attentional space. For this purpose, we modeled in a cognitive architecture the essential cognitive functions necessary to learn and used bottom-up attention as input to a reinforcement learning algorithm. The results show that the robot can learn on attentional and sensorial spaces. By comparing various action schemes, we find the set of actions for successful learning.
A Secure ECC-Based Authentication Scheme to Resist Replay Attacks for the IoT

Because of the resource-constrained characteristic of IoT devices, it is difficult to achieve user authentication in IoT by using classical Internet's authentication schemes. The reply attack is a classical attack on authentication protocols. The timestamp mechanism by maintaining clock synchronization is generally used in the exiting schemes for IoT. We propose a lightweight authentication scheme based on elliptic curve cryptography (ECC) in this paper. The proposed scheme introduces an improved challenge-response mechanism instead of the timestamp. Thus, storage and computation cost of generating and maintaining timestamp can be eliminated.
Academic Paper Personalized Search Based on Interest Subject Tracking and Search Performance Evaluation

Web services that provide academic papers present information based on their own search algorithms. However, since it only judges the relevance between the query and the document without understanding the user’s interest field and the retrieval intent, there is a limit to presenting the optimal information for each individual. In this paper, the user search intention at the time of retrieval is identified based on the subject classification and it is determined as the area of interest. In particular, we propose personalized search as a way to provide information suitable for individual interests when homonym or different meanings are used in different research fields. The proposed personalized search performance based on the subject classification tracking showed higher search performance than the comparison target.