Reviewerly/exHarmony huggingface dataset:
A Comprehensive Study on ARM Disassembly Tools

Embedded devices are becoming ubiquitous, and ARM is becoming the dominant architecture for them. Meanwhile, there is a pressing need to perform security assessments for these devices. Due to different types of peripherals, emulating the software, i.e., firmware, of these devices in scale is challenging. Therefore, static analysis is still widely used. Existing works usually leverage off-the-shelf tools to disassemble stripped ARM binaries and (implicitly) assume that reliably disassembling binaries is a solved problem. However, whether this assumption really holds is unknown. In this paper, we conduct the first comprehensive study on ARM disassembly tools. Specifically, we build 1,896 ARM binaries (including 248 obfuscated ones) with different compilers, compiling options, and obfuscation methods. We then evaluate them using eight state-of-the-art ARM disassembly tools (including both commercial and noncommercial ones) in three different versions on their capabilities to locate instruction boundary, function boundary, and function signature. Instruction and function boundary are two fundamental primitives that the other primitives are built upon while function signature is significant for control flow integrity (CFI) techniques. Our work reveals some observations that have not been systematically summarized and/or confirmed. For instance, we find that the existence of both ARM and Thumb instruction sets, and the reuse of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BL</i> instruction for both function calls and branches bring serious challenges to disassembly tools. Our evaluation sheds light on the limitations of state-of-the-art disassembly tools and points out potential directions for improvement.
Algorithms

The chapter provides formal descriptions of the major quantum algorithms, along with their Q# implementations. It starts with the Deutsch–Jozsa algorithm and the Bernstein–Vazirani problem. Next, it moves to Grover’s algorithm and covers the concept of amplitude amplification through inversion about the mean. Following that, Quantum Fourier Transform is introduced, as well as the related concept of quantum phase estimation. With all these covered, the chapter concludes with Shor’s algorithm, which makes use of the previously introduced ideas.
Aggregation of Semantically Similar News Articles with the Help of Embedding Techniques and Unsupervised Machine Learning Algorithms: A Machine Learning Application with Semantic Technologies

Business news helps leaders and entrepreneurs in decision-making every day. This involves making corporate strategies, taking marketing decisions, planning operations, investing human capital, etc. This news gives a complete idea to leaders and entrepreneurs about what is happening in the corporate world. They maintain track of all mergers and takeovers and make interested people informed. Today, it is essential for people to keep themselves updated about corporate business. As there are so many news websites and the same news article gets published on each of these websites with a little changed title. As a consequence of which people have to spend far longer trying to find information than the time they have to catch up on the news. So, it would be very helpful for such people, if clusters of semantically similar news articles from different websites could be created, and reading only one news item from one cluster will be sufficient. This chapter will explain a few approaches to aggregate similar news articles. The very first step is to collect the data. Initially, for developing the model, data is collected from sites such as Kaggle, UCI, etc. After the model is developed, real-time data can be collected using the news API. The second step is to preprocess the collected data which involves subtasks such as Tokenization, Stop-Word Removal, Stemming/Lemmatization, Case Transformation, etc. The third step here is Embedding Text to Vectors, using various embedding techniques such as Bag-of-Words, TF-IDF, Word2Vec, etc. The next task here is to make clusters of these embedded vectors or numbers, using various unsupervised learning algorithms such as K-means, agglomerative, etc. Finally, the last step here is to find a comparison of various combinations of embedding techniques and clustering algorithms.
Extended Mouth/Tongue Gesture Recognition Module for People with Severe Motor Dysfunction

We researched gesture interfaces for people with motor dysfunction who cannot use normal interface switches. For this purpose, we have developed nine gesture recognition modules. One of them is the tongue recognition module that detects whether the tongue is in or out for the switch interface. In the process of collecting data on various types of mouth and tongue gestures and user’s demands, we discovered three useful types of mouth-related gestures: mouth open/close, tongue in/out, and movement around the lips. These gestures are also useful when performed simultaneously. We conducted several experiments using data of people with motor dysfunction and inspected the conditions for real use. Furthermore, we conducted a basic experiment with healthy people to recognize three gestures simultaneously.
Information Tracking Extraction for Emergency Scenario Response

With the frequent occurrences of emergencies, scenario response is proposed by researchers for emergency management. On the one hand, extracting emergency-related information is necessary and important for describing emergency scenario correctly; On the other hand, some emergency-related information evolve dynamically with time going on. To this end, a method for information extraction based on event tracking (called as information tracking extraction) for emergency scenario response is proposed. Based on the emergency tracking, it firstly judges the type of the emergency; secondly extracts time, location, casualty and loss based on multi strategies; finally adopts E-charts to visualize the extraction results. Experimental results show the proposed method can achieve high precision and recall, and the visualization of the tracking extraction results can display the dynamic evolution process better, which is very helpful to the scientific decision-making in the emergency management.
Improve Convolutional Neural Network Pruning by Maximizing Filter Variety

Neural network pruning is a widely used strategy for reducing model storage and computing requirements. It allows to lower the complexity of the network by introducing sparsity in the weights. Because taking advantage of sparse matrices is still challenging, pruning is often performed in a structured way, i.e. removing entire convolution filters in the case of ConvNets, according to a chosen pruning criteria. Common pruning criteria, such as $$l_1$$ -norm or movement, usually do not consider the individual utility of filters, which may lead to: (1) the removal of filters exhibiting rare, thus important and discriminative behaviour, and (2) the retaining of filters with redundant information. In this paper, we present a technique solving those two issues, and which can be appended to any pruning criteria. This technique ensures that the criteria of selection focuses on redundant filters, while retaining the rare ones, thus maximizing the variety of remaining filters. The experimental results, carried out on different datasets (CIFAR-10, CIFAR-100 and CALTECH-101) and using different architectures (VGG-16 and ResNet-18) demonstrate that it is possible to achieve similar sparsity levels while maintaining a higher performance when appending our filter selection technique to pruning criteria. Moreover, we assess the quality of the found sparse subnetworks by applying the Lottery Ticket Hypothesis and find that the addition of our method allows to discover better performing tickets in most cases.
Effects of a Web System for Practice Problems which Provides Immediate Feedback on Students in Remote Class

We introduced a web system for practice problems which provided immediate feedback into remote class and found the following things. The average score of the midterm exam was significantly higher than that of previous year. When we continued to deliver the contents of remote class in face-to-face class which was gradually restarted after the midterm exam, average score of the final exam in “the standard class” (“the basic class”) was higher (significantly lower) than that of previous year. According to surveys of questionnaire, the demerit we assumed when building the remote class was not off-target and our web system for practice problems and remote class got considerable support from the students.
A Comprehensive Investigation of Workflow Scheduling in Cloud Computing Environment

The cloud resource scalability feature allows the users' applications to meet its need during runtime or before execution and hence it required to be organized dynamically as needed. These scalable and distributed features of the cloud resources allow workflow management systems to meet the expectation of the service provider and customer. The service-level agreement (SLA) is a major concern in workflow algorithms. It also looks at the economic benefits for service providers and customers. Due to these multi-objective natures of workflow scheduling and various constraints imposed by user and cloud environment, a large number of algorithms are suggested by various researchers. There is not a single algorithm proposed by researchers which handle all known constraint imposed by user and service provider. At the end of this research paper, the authors have suggested that workflow scheduling for cloud environments is an optimization problem.
Multi-task Parallel: A Tumor Segmentation Approach of Specific Task Attention

It is of great significance to make full use of the complementary advantages of different modality imaging information for improving the accuracy of tumor segmentation and formulating precise radiotherapy plans. This paper proposed a multi-tasking parallel training method, which combined the attention mechanism of specific tasks to mine the effective information of different modals. It has three parallel learning networks based on parameter sharing, including CT segmentation network, MRI segmentation network, and the joint learning network of similarity measurement between CT and MRI images. CT and MRI segmentation networks learned their specific task features, and used the attention module of specific tasks to enhance the utilization of effective features while learning shared features. The similarity measurement learning network jointly learned the similarity between CT and MRI images, and combined the specific task features shared by CT and MRI segmentation networks to segment multimodal tumor images. Comparing the results of single-modal and multi-modal tumor image segmentation, it is proved that multi-modal segmentation can provide more abundant features and effectively locate the tumor location, especially in the fuzzy adhesion region of the tumor boundary. In addition, other multi-modal image segmentation methods were compared, and the results also prove that the multi-task learning method is suitable for multi-modal image segmentation and has achieved better segmentation results.
A Study on the Recognition and Memory of Shapes for the Elderly

This research is mainly aimed at exploring the recognition and memory ability of basic geometric shapes in the elderly, in order to understand the status of recognition and memory ability of shapes of different complexity in the elderly. It is mainly carried out through the experimental method. The experimental method is carried out with 12 shape test samples, and the accuracy of shape recognition and different time differences are discussed. The subjects were divided into 10 elderly people in the experimental group and 10 college students over the age of 20 in the control group. The results show that there is a gap in the accuracy of shape recognition among the elderly. The more complex the shape, the lower the recognition ability. In addition, both groups of subjects made errors in recognizing the obliquely angled figures. The time difference between 5 s and 30 s between shape F (original hexagon) and shape I (hexagon with rounded corners) is significantly different, and shape I obtains the lowest recognition accuracy rate at a 5-s interval for the elderly. The experimental results also show that the front and back positions of the shape images in the sequence have an impact on the memory ability.
Information Technology in the Pedagogical Training of Pop Vocalists at the University

Information technologies are actively used today in all areas of higher education. A feature of higher pedagogical education in the preparation of pop vocalists is the obligatory use of such technologies. Today, none of the students of pop performance can do without electronic simulators that contribute to the formation of vocal technique and the development of musical abilities. Pop vocalists studying at pedagogical universities, in addition to daily training their voice, should accumulate knowledge about the history of pop performance, ways to promote their creativity on social networks and, of course, knowledge in the field of musical pedagogy. The traditional forms of pedagogical training of students are lectures and practical classes, the content of which is related to acquainting students with vocal pop schools, methodological systems of certain teachers. However, in the content of such training, not enough attention is yet paid to information technologies and their pedagogical potential for teaching a particular child or adult who will be patronized by students-future pop vocal teachers. The purpose of this article is to reveal the pedagogical potential of various information technologies to expand the methodological knowledge of students studying pop vocal at the Pedagogical University. The technologies are chosen by the university teacher to demonstrate their pedagogical feasibility, and then by the students themselves, will contribute to their professional and pedagogical development.
A Unified Rgb-T Crowd Counting Learning Framework

In this paper, a novel Unified RGB-T Crowd Counting Learning Framework (UCCF) is proposed, which utilizes an image fusion network architecture and a crowd counting network architecture to estimate the density map and count results simultaneously. The purpose of our framework is to fuse two modalities, including visible and thermal infrared images, that exploit the complementary information to accurately count the dense population and construct the end-to-end training process. To this end, we first propose the unified RGB-T crowd counting learning framework to complete the image fusion task and crowd counting task simultaneously by redesigning the unified training loss function. Also, to further narrow the gap between the two models and simplify the end-to-end training process, we design the Assisted Learning Module (ALM) by merging the density map feature into the image fusion encoding process. Meanwhile, we propose an Extensive Context Extraction Module (ECEM) and apply the Multi-domain Attention Block (MAB) to further improve the counting accuracy. Experiments on the DroneRGBT dataset have verified the effectiveness and robustness of the proposed model.
The Real Effects of Macroprudential Policy: Evidence from Micro Data

This paper investigates the real effects of macroprudential policy (MaPP) using individual data from 122 countries. The empirical analysis shows that MaPP increases savings and decreases borrowing. These effects are then disaggregated by policy instrument, interest rate and country income level. The effects of MaPP on individual behavior depend to a large extent on the policy instrument and the country income level and to a lesser extent on the interest rate. These findings stand up to a variety of endogeneity tests that include propensity score matching and an instrumental variable approach.
Robust and Efficient Velocity Estimation for MAVs With an RGB-D Camera

Micro Aerial Vehicles (MAVs) have become increasingly popular due to their wide applications in many areas. For autonomous navigation and safe control of MAVs, it is essential to have accurate and reliable velocity and position estimation. However, due to limited computational power and payload, it is still challenging for autonomous operation of MAVs in complex environments. In this paper, we propose a robust and efficient velocity estimation framework for MAVs with a single downward-facing RGB-D camera, which is able to provide metric velocity estimation in three dimensions as well as yaw rate in real-time without the fusion of additional sensors. Unlike traditional algorithms, our method uses kernel cross-correlators (KCCs) to efficiently determine optical flow for motion estimation, which does not rely on costly feature extraction and matching process. Moreover, we utilize depth images to estimate the vertical velocity of MAVs without the assumption of a flat ground. Autonomous flight tests on a quadrotor in complex environments demonstrate the robustness and efficiency of our method.
User Interest-Based Movie Recommender System Using Hybrid Computing

Multimedia is considered as one of the best sources of entertainment. People of all age groups love to watch movies. Movie Recommender System is essential in our social lives as it aims to enhance the field of entertainment. The proposed work is based on the movie recommendation system which caters to all the requirements of the user. This paper provides related content to the end-users out of relevant and irrelevant content on the Internet. The problem statement is to generate accurate, efficient, and personalized recommendations for the user. This paper delivers three functionalities, firstly it displays all the information related to the movie which is obtained by making an API call, secondly analyzes the user's interest dimension, and finally recommends personalized movies of the user's interests by using Cosine Similarity. The proposed procedure is different from the current approaches in various aspects. All the parameters and constraints are well taken in the paper and results are compared with the existing algorithms available for movie recommendations.
A Procedural Model for Utilizing Case-Based Reasoning in After-Sales Management

The identification of spare parts as an integral part of maintenance processes especially in mechanical and plant engineering requires expert knowledge. The main reason is an undeniable information gap in existing service documentations. Knowledge-based approaches may facilitate and ensure standardized management of service kits. This paper introduces an integrated procedural model deploying case-based reasoning (CBR) to document expert knowledge, and to automatize spare parts identification processes. The application of the procedural model is exemplified in an industrial use case, which deepens insights into the case derivation and solution generation steps using necessary similarity measures and related algorithms.
Stereo Matching Algorithm Based on Census Transform and Segment Tree Cost Aggregation

Stereo matching is a complicated process due to the uneven distribution of textures, brightness and exposure to the image pair. Aiming to improve the accuracy of the stereo matching algorithm and increase the quality of the disparity maps, various stereo matching algorithms had been proposed recently. This paper proposed the stereo matching algorithm based on Census Transform at matching cost computation, followed by the non-local Segment Tree cost aggregation. WTA strategy is used in cost optimization and a weighted median filter is used in the final stage of the stereo matching process. The experimental results show that the combination of CT and ST is working effectively in terms of the absolute error and is comparable to the combination of CT and GF.
DTATrans: Leveraging Dynamic Token-Based Quantization With Accuracy Compensation Mechanism for Efficient Transformer Architecture

Models based on the attention mechanism, i.e., transformers, have shown extraordinary performance in natural language processing (NLP) tasks. However, their memory footprint, inference latency, and power consumption are still prohibitive for efficient inference at edge devices, even at data centers. To tackle this issue, we present an algorithm-architecture co-design named DTATrans. We find empirically that the tolerance to the noise varies from token to token in attention-based NLP models. This finding leads us to dynamically quantize different tokens with mixed levels of bits. Furthermore, we find that the overstrict quantization method causes a dilemma of the model accuracy and model compression ratio, which impels us to explore a method to compensate for the model accuracy when the compression ratio is high. Thus, in DTATrans, we design a compression framework that: 1) dynamically quantizes tokens while they are forwarded in the models; 2) jointly determines the ratio of each precision; and 3) compensate the model accuracy by exploiting lightweight computing on the 0-bit tokens. Moreover, due to the dynamic mixed-precision tokens caused by our framework, previous matrix-multiplication accelerators (e.g., systolic array) cannot effectively exploit the benefit of the compressed attention computation. We thus design our transformer accelerator with the variable-speed systolic array (VSSA) and propose an effective optimization strategy to alleviate the pipeline-stall problem in VSSA without hardware overhead. We conduct experiments with existing attention-based NLP models, including BERT and GPT-2 on various language tasks. Our results show that DTATrans outperforms the previous neural network accelerator Eyeriss by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$16.04\times $ </tex-math></inline-formula> in terms of speedup and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.62\times $ </tex-math></inline-formula> in terms of energy saving. Compared with the state-of-the-art attention accelerator SpAtten, our DTATrans achieves at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.62\times $ </tex-math></inline-formula> speedup and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.22\times $ </tex-math></inline-formula> energy efficiency improvement.