Reviewerly/exHarmony huggingface dataset:
Synthesizing Legacy String Code for FPGAs Using Bounded Automata Learning

The adoption of hardware accelerators, such as field-programmable gate arrays, into general-purpose computation pipelines continues to expand, but programming models for these devices lag far behind their central processing unit (CPU) counterparts. While high-level synthesis (HLS) can help port some legacy software, many programs perform poorly without manual, architecture-specific optimization. We propose an end-to-end approach combining dynamic and static analyses to learn a model of functional behavior for off-the-shelf legacy code and synthesize a hardware description from this model. Our prototype implementation can correctly learn functionality for string kernels that recognize regular languages and provides a near approximation otherwise. We evaluate our prototype tool on a benchmark suite of real world, legacy string functions mined from GitHub and successfully synthesize—without modification or annotation—over 80% (72% exactly and a further 11% approximately). Traditional HLS, only after extensive modification and custom testbench generation, can synthesize the same number of benchmarks, but with results that have higher hardware requirements and lower maximum clock rates.
The Use of Information Technology for Project Training at IRNTU

The transformation of the role of universities in the global context - from educational “centers” to “growth points” of business, science and personality - is a phenomenon of the beginning of the XXI century, which causes a significant change in approaches to the use of educational technologies used. The challenges of the “new” knowledge economy pose completely new non-trivial tasks for educational systems. The formation of a competitive, mobile, active and independent personality, demanded by the labor market, requires the disclosure of the “flexible skills” of the graduate - the ability to effective teamwork, project management and decision-making skills, the ability to self-development and creative thinking. In this regard, the proactive project management model deserves special attention. At the same time, a significant role should be given to studying the possibilities of digital technologies widely used by the business environment. This study is devoted to the analysis of the practice of transformation of educational programs of the bachelor’s degree of the largest university in Eastern Siberia - Irkutsk National Research Technical University. The article pays attention to the use of information technologies for training personnel in the field of project management, the main task of which is the practical orientation to work with relevant business projects, the launch of start-ups, the commercialization of scientific and technical developments of the university, the creation of an entrepreneurial environment.
ABRaider: Multiphase Reinforcement Learning for Environment-Adaptive Video Streaming

HTTP-based video streaming technology is widely used in today’s video delivery services. The streaming solution uses the adaptive bitrate (ABR) algorithm for better video quality and user experience. Despite many efforts to improve the quality of experience (QoE), it is very challenging for ABR algorithms to guarantee high QoE to all users in various environments. The video streaming circumstances in the real world have become even more complicated by the proliferation of mobile devices, high-quality content, and heterogeneous configurations of video players. Many ABR algorithms aim to find monotonous strategies that generally perform well without focusing on the complexity of the environments, which can degrade performance. In this paper, we propose ABRaider that guarantees high QoE to all users in a variety of environments in the real world while being generalized with multiple strategies and specialized in each user’s environment. In ABRaider, we propose multi-phase RL consisting of offline and online phases. In the offline phase, ABRaider integrates the strengths of the ABR algorithms and develops policies suitable for various environments. In the online phase, ABRaider focuses on specializing in the environments of individual users by leveraging the computational power of the clients. Experiment results show that ABRaider outperforms existing solutions in various environments, achieving 19.9% and 42.2% QoE improvement in VoD and live streaming, respectively.
Teaching the Graphic in Times of Pandemic. Cape Cod and the Bauhaus

The traditional form of education at any of the levels traditionally tested has been disturbed by the global developments in the pandemic and its unpredictable consequences. It is architecture, and in particular its graphic facet, that has been the most marked by obvious issues such as the tangibility of experience, direct perception or the ability to enjoy the shared outside experience. To combine simultaneously two parallel working routines: A first experimental, real and palpable, that analyzes the direct perception of the architectural fact, with a second subject to the rigor of non-face teaching, whose limits of exploration require the help of numerous disciplines bordering on architecture, geography, topography, urbanism or sociology allow the diversification of the teaching of form and space to the desired goals. The course Analysis of Architectural Forms 1, is a graphical subject that has a concrete and consensual program, consolidated over the years; this allows us to face the challenge of a proven and versatile teaching, despite the fugiousness of the semesters, in which work and quality and undisciplinarity are ensured whatever the circumstances.
A Survey of Techniques for Constructing Mongolian Domain-Specific Knowledge Graph

Since Google proposed the concept of knowledge graph in 2012, both academic and industrial communities have paid increasing attention to it because of its strong ability of representing information about knowledge and reasoning. Although a lot of studies and applications of knowledge graph have been released in recent years, it is still very few that research related to how to build a knowledge graph involved with Mongolian resources. Therefore, this chapter provided a survey on constructing a high-quality Mongolian vertical domain knowledge graph, and which techniques should be used. First, we introduce the state of the art in the construction of knowledge graph. Second, discuss and analyze the challenges in building a Mongolian domain-specific knowledge graph such as in the financial domain. Finally, we propose an artificial intelligence technique for named entity recognition for Mongolian language resources.
Speaker Identification Using Multiple Features and Models

Speaker identification is an upcoming boon in this modern technology. It is basically a “one on many mapping” technique in which the speaker can be identified by matching the unknown speaker’s speech with templates of all speakers, called as speaker identification or in other words, an utterance from unknown speaker is analysed and compared with speech model of known speakers. The speaker identification process is segregated into two phases, namely the training phase and the test phase. In training phase, the input of 15 speakers including both male and female speaker’s speech utterances are taken to obtain the individual speaker models by extracting the features such as gammatone cepstral coefficient (GTCC) and mel-frequency cepstral coefficients (MFCC). In test phase, a random utterance spoken by each speaker is subjected for comparison with the speaker models obtained by k-means clustering so as to find out the particular speaker accurately. Finally, a comparison is made between the MFCC and GTCC feature vectors in terms of accuracy in predicting the exact speaker. This speaker identification technology (SIT) is used in various applications such as in voice biometrics for authentication purpose, in surveillance for eavesdropping telephone conversations and in forensics department for backtracking suspect’s voice during crimes. It is also used in Google’s speech recognition system so as to unlock the gadgets with the speaker’s voice that is used as a password for privacy protection. The stated problem objective’s accuracy is expected to be in the range 80–90%. In addition to the two feature extractions, convolutional neural network is deployed for the same set of speaker’s speech utterances, thereby enhancing the accuracy beyond 95%.
Search-Theoretic Approach to Cryptocurrency Adoption and Financial Inclusion

As being unbanked is one of the motivations for using cryptocurrency, this study analyzes the financial inclusion of unbanked individuals that results from the use of peer-to-peer cryptocurrency transactions. By extending the Kiyotaki-Wright model, we construct a dual- currency economy in which traditional money and private cryptocurrency coexist. In this economy, some of the agents are banked while others are unbanked; thus, the traditional payment services provided by banks are limited. Our theoretical model yields a long-run equilibrium condition in which the cryptocurrency is perceived as valuable even though it is not a fully accepted currency. Our analysis suggests that the unbanked population is a contributing factor in bitcoinâ€™s fundamental value.
A Semantic Segmentation-Based Collision Recognition Method for Slalom Poles

Motorcycles are a widely used means of transportation. Many countries require drivers to pass the motorcycle skills test, among which Motorcycle Slalom through Poles is a necessary test. This test mainly relies on manual assessment in China, which cannot ensure accuracy, fairness, and efficiency. This paper proposes a collision recognition method for slalom poles. Based on the real-time image provided by the on-site camera, the angle change of the pole is calculated to recognize the collision between the motorcycle and the pole. First, we propose a Fast Flow Alignment Module (FFAM) based on semantic flow alignment, which can effectively improve the semantic transfer efficiency of the semantic segmentation network. Then we build a lightweight semantic segmentation network using the FFAM, which can accurately and quickly calculate the semantic information of poles from images. Finally, we post-process the semantic information of the poles to obtain the dynamic angle change of the poles. When the angle changes greatly, we judge that a collision occurs. Experiments demonstrate that our proposed network achieves 77.6% mIoU with a frame rate of 167 fps on the Motorcycles Slalom through Poles dataset, significantly outperforming existing networks. And the angle changes of poles obtained by our method are accurate within ±0.1° error, which is sufficient for Motorcycle Slalom through Poles Test.
A Powerful Correspondence Selection Method Based on Machine Learning

Correspondence selection is an indispensable process in 3D rigid data matching. The success of 3D data matching largely depend on a good correspondence selection method. For this purpose, a novel correspondence selection method is proposed in this paper. First, two geometric constraints, one of which is proposed in this paper, are used to compute the compatibility score between two correspondences. Then, the feature vector of one correspondence is constructed according to the compatibility scores between the correspondence and others. Eventually, an SVM classifier is trained to classify the correct and incorrect correspondences by using the feature vectors. The experimental results demonstrate that our method can well choose the right correspondences and get high precision and F-score performance. Also, our method has the best robustness to noise, point density variation and partial overlap compared to the other methods.
GAMNet: Global Attention via Multi-Scale Context for Depth Estimation Algorithm and Application

Deep neural networks significantly enhance the accuracy of the stereo-based disparity estimation. Some current methods suffer from inefficient use of the global context information, which will lead to the loss of structural details in ill-posed areas. To this end, we design a novel stereo network GAMNet composed of three core components (GDA, MPF, DCA) for estimating the depth prediction in challenging real-world environments. Firstly, we present a lightweight attention module, integrating the global semantic cues for every feature position across the channel and spatial dimensions. Next, we construct the MPF module to fuse the diverse semantic and contextual information from different levels of the feature pyramid. Finally, we aggregate cost volume with a stacked encoder-decoder composed of our DCA module and 3D convolutions, filtering the transmission of matching clues and capturing the rich global contexts. Substantial experiments conducted on KITTI 2012, KITTI 2015, SceneFlow, and Middlebury-v3 datasets manifest that GAMNet surpasses preceding methods with contour-preserving disparity predictions. In addition, we propose the first 3D scene reconstructions linear evaluation strategy on spatial grasping point for the end-to-end stereo networks in an unsupervised mode, and deploy it on our designed robot vision-guided system. In application experiments, our method can produce densely high-precision 3D reconstructions to implement the grasping task in complex real-world scenes, and achieve excellent robust performance with competitive inference efficiency. Source code: https://github.com/yangH8/GAMNet .
An Argument Dialectical Analysis of the Russell-Copleston Debate

I will present an argument dialectical analysis of the Russell-Copleston debate. As explained in chap. 9 this will be a logical analysis. Hence, I will focus neither on the series of actions performed by Russell and Copleston during the debate nor on the procedure followed by them in doing so, but on the relationships between the arguments advanced by them. I will be guided by the logical question par excellence “Shall we accept this claim on the basis of the reasons put forward in support of it?”, as addressed by the protagonists of the debate through a succession of arguments and counter-arguments. What I want to capture are the macro-arguments that Russell and Copleston construct successively and interactively in the course of the debate -what might be termed, perhaps, the logical structure of the debate.
Sentiment Analysis of Images using Machine Learning Techniques

Sentiment analysis is the process of identifying the idea of a text. People share the comments on social media stating their knowledge of the event and would like to know if most other people had a good or bad experience at the same event. This distinction can be made through Emotional-Analysis. Sentiment analysis captures informal text comments, posts and images from all comments shared by different users and classifies comments into different categories as neutral, negative or positive. This is also called as polarity separation. Various different types of ML and in-depth learning methods may be utilised in Sentiment Analysis like Support Vector Machines, NB, Haar Cascade, LBPH, CNN, etc. Emerging rise in popularity in Social Media has established a trend of posting images in restaurants to express their opinion on the food, ambience, etc which can be a useful resource to obtain opinion and feedback from the Customers. In this paper, the implementation of Sentiment Analysis on images containing users along with their faces from the restaurants review revealing it more efficacious in classifying and identifying sentiments of review-images.
AI and AI-Human Based Salesforce Hiring Using Interview Videos

We study the problem of AI and AI-human based screening (eliminating bottom candidates) and selection (hiring top candidates) in salesforce hiring. Using videos of structured interviews and ratings from multiple recruiting experts on standard performance criteria, we develop an AI model of salesforce “skill” prediction by extracting theory-relevant objective measures of interviewee performance from videos. Using the model, we address two issues: First, to aid interpretability, we assess what mode of unstructured data from the interview (text, audio and video) and what specific behaviors (e.g, body language, conversation style) drive AI predictions of salesperson’s ability. We find that while screening decisions are heavily impacted by audio-visual features like “energetic voice” and “open posture”; selection relies more heavily on textual content and linguistic features such as being precise and quantitative. We also find that there is an optimal amount of hand gesturing, body posture movement and conversational interactivity for success in selling. Second, we consider a hybrid AI-human model where we augment AI predictions with human interventions in a Bayesian framework. The hybrid model improves accuracy but increases cost (of human labor). Hence, we propose a cost-effective way of deploying AI in salesforce hiring with humans in the loop — use AI for screening and augment it with human judgments based on early stages of interview for selection
A Blockchain-Based IoT Data Secure Vickery Auction System

Traditional centralized architectures for IoT data suffer from single points of failure in terms of data storage and access control, and IoT data does not fully realize its value as a digital asset. In response to the above challenges, we propose a decentralized storage and lightweight access control method for IoT data based on blockchain technology, and put forward an auction system to commercialize the IoT data. Firstly, a blockchain-based data storage method proposed using Inter Planetary File System (IPFS), which ensures the data is immutable and traceable. Secondly, in this paper, we propose a distributed lightweight Capability-Based Access Control (CapBAC) based on blockchain, in which a time dimension is added so that no additional token revocation operation is required. Thus, lightweight access control is achieved under IoT devices with limited computing power. Finally, we come up with a blockchain-based Vickery auction system to sell the data to the highest bidder in demand. This auction effectively protects the privacy of bidders and does not require third-party intermediary fees. And it can ensure fairness and non-repudiation of the auction. By analyzing the entire system process, we guarantee secure storage of IoT data. Besides, we can achieve lightweight access control and realize the value of the data based on the blockchain technology.
Research of Vision-Based Approach for Assisted-Robot

Recently, the new trend to integrate image processing knowledge into robot control has been gained many significant achievements. With this development of technology, the robot has become a more friendly and useful solution in daily life. In addition to education, it also supports people at work. However, there are still invisible barriers in employing the interaction with a robot. In this paper, a novel method to track a target by using a digital camera is presented to lessen the burden works. An autonomous robot is provided an excellent ability to recognize, estimate and follow the movement of a target. Besides, the structure of the lift mechanism is newly introduced in order to offer a flexible motion for the camera in the warehouse. In the test scenario, an autonomous robot is trained to track workers and share their labor costs. From the results, it can be seen clearly that the author’s approach is effective, feasible, and applicable in the industrial sector.
Detection of Anomalies in Traffic Flows with Large Amounts of Missing Data

Anomaly detection plays an important role in traffic operations and control. Missingness in spatial-temporal datasets prohibits anomaly detection algorithms from learning characteristic rules and patterns due to the lack of large amounts of data. This paper proposes an anomaly detection scheme based on Gaussian process models for sparse traffic flow data with missingness. The dataset is provided by the National Science Foundation (NSF) in conjunction with the National Geospatial-Intelligence Agency (NGA), and it consists of thousands of labeled traffic flow records for 400 sensors from 2011 to 2020. Each sensor is purposely downsampled by NSF and NGA in order to simulate missing completely at random, and the missing rates are 99%, 98%, 95%, and 90%. Hence, it is challenging to detect anomalies from the sparse traffic flow data. The proposed scheme makes use of traffic patterns at different times of day and on different days to recover the complete data. Features generated from Gaussian process regression are used in a logistic regression model leading to high prediction accuracy. The proposed anomaly detection scheme is computationally efficient by allowing parallel computation on different sensors. We provide a detailed description of the data engineering and the anomaly detection process, and we evaluate the proposed method through real data analysis.
Multi-Objective Optimization Robot Navigation Through a Graph-Driven PSO Mechanism

In real-world robot applications such as service robots, mining mobile robots, and rescue robots, an autonomous mobile robot is required to visit multiple waypoints that it achieves multiple-objective optimizations. Such multiple-objective optimizations include robot travelling distance minimization, time minimization, turning minimization, etc. In this paper, a particle swarm optimization (PSO) algorithm incorporated with a Generalized Voronoi diagram (GVD) method is proposed for a robot to reach multiple waypoints with minimized total distance. Firstly, a GVD is used to form a Voronoi diagram in an obstacle populated environment to construct safety-conscious routes. Secondly, the sequence of multiple waypoints is created by the PSO algorithm to minimize the total travel cost. Thirdly, while the robot attempts to visit multiple waypoints, it traverses along the edges of the GVD to form a collision-free trajectory. The regional path locally from waypoints to nearest nodes or edges needs to be created to join the trajectory. A Node Selection Algorithm (NSA) is developed in this paper to implement such a protocol to build up regional path from waypoints to nearest nodes or edges on GVD. Finally, a histogram-based local reactive navigator is adopted for moving obstacle avoidance. Simulation and comparison studies validate the effectiveness and robustness of the proposed model.
Real-Time Face Recognition for Organisational Attendance Systems

Today we are in an era of feasible biometric solutions to the age-old problem of verifying personal identity. As a form of identity that is verified through inseparable and unique characteristics of a person: their face - facial recognition is now one of the most popular methods in use. We propose an automated real-time facial attendance system where the users can verify their identity without physical contact with any surface. This is achieved through a conjunction of a mobile and platform-independent web application over a shared cloud database. The system is powered by a Face Recognition module to authenticate the users and demonstrated 99.7% of test accuracy, with an improved true positivity rate of 96.14% compared to some existing literature. Our system processes and aligns input face images before utilizing a deep convolutional neural network model to recognize the user’s identity. To test the efficacy of our system, we have built a database of over 800 unique individuals of Indian descent. Our tests showed a superior true-positivity rate on our tweaked model demonstrating its efficacy in comparison with the reference literature. We also share some of the methods employed to raise system redundancy and minimize false negativity; a vital metric in any authentication application.