Reviewerly/exHarmony huggingface dataset:
LAPE: A Lightweight Attestation of Program Execution Scheme for Bare-Metal Systems

Unlike traditional processors, Internet of Things (IoT) devices are short of resources to incorporate mature protections (e.g. MMU, TrustZone) against modern control-flow attacks. Remote (control-flow) attestation is fast becoming a key instrument in securing such devices as it has proven the effectiveness on not only detecting runtime malware infestation of a remote device, but also saving the computing resources by moving the costly verification process away. However, few control-flow attestation schemes have been able to draw on any systematic research into the software specificity of bare-metal systems, which are widely deployed on resource-constrained IoT devices. To our knowledge, the unique design patterns of the system limit implementations of such expositions. In this paper, we present the design and proof-of-concept implementation of LAPE, a lightweight attestation of program execution scheme that enables detecting control-flow attacks for bare-metal systems without requiring hardware modification. With rudimentary memory protection support found in modern IoT-class microcontrollers, LAPE leverages software instrumentation to compartmentalize the firmware functions into several ”attestation compartments”. It then continuously tracks the control-flow events of each compartment and periodically reports them to the verifier. The PoC of the scheme is incorporated into an LLVM-based compiler to generate the LAPE-enabled firmware. By taking experiments with several real-world IoT firmware, the results show both the efficiency and practicality of LAPE.
Design of Robust State Estimation Filter in The Presence of Uncertainty, Fault, and Unknown Input

In this study, state estimation problem is considered for a class of uncertain time varying nonlinear stochastic system. The considered system is subject to faults, disturbance, uncertainty and nonlinearity. For this purpose, a robust extended Kalman filter is proposed. So, we design robust extended Kalman filter and the upper bound of state estimation error covariance is yield for all stochastic uncertainties. The performance of the filter is demonstrated on the state estimation for a nonlinear helicopter unmanned aerial vehicle (HUAV). The results of the proposed filter are simulated and it is compared with conventional nonlinear robust filter (NRF) and EKF. Result shows the better efficiency of the proposed method.
MF-COTE: A chinese opinion target extraction model based on multiple features

Recently, deep learning methods have been applied to deal with the opinion target extraction (OTE) task with fruitful achievements. On the other hand, since the features captured by the embedding layer can make a multiple-perspective analysis from a sentence, an embedding layer that can grasp the high-level semantics of the sentences is of essence for processing the OTE task and can improve the performance of model into a more efficient manner. However, most of the existing studies focused on the network structure rather than the significant embedded layer, which may be the fundamental reason for the problem of relatively poor performance in this field, not mention the Chinese extraction model. To compensate these shortcomings, this paper proposes a model using multiple effective features and Bidirectional Encoder Representations from Transformers (BERT) on the architecture of Bidirectional Long Short-Term Memory (BiLSTM) and Conditional Random Field (CRF) for Chinese opinion target extraction task, namely MF-COTE, which can construct features from different perspectives to capture the context and local features of the sentences. Besides, to handle the difficult case of multiple nouns in one sentence, we innovatively propose noting words feature to regulate the model emphasize on the noun near the transition or contrast word, thus leading a better opinion target location. Moreover, to demonstrate the superiorities of the proposed model, extensive comparison experiments are systematically conducted compared with other existing state-of-the-art methods, with the F1-score of 90.76%, 92.10%, 89.63% on the Baidu, the Dianping, and the Mafengwo dataset, respectively.
Augmented Reality Circuit Learning

Building electronic circuits is one of the most common hands-on activities in learning STEM subjects. Beginning students often find it difficult to translate a circuit schematic into the construction of the physical circuit on a breadboard. In this paper, we describe an Augmented Reality circuit learning software that can provide students with step-by-step instructions by placing virtual circuit components on a physical breadboard. We propose a novel image processing pipeline that can robustly identify the planar structure of a breadboard, even in the presence of occluding circuit components on the breadboard. Using a commercially available library of 3D circuit component models, the estimated 3D structure of the breadboard allows us to render arbitrary circuit components on it in real time. Experimental results demonstrate that our algorithms are accurate and can produce realistic-looking virtual circuits.
Design & Implementation of Digital Guitar Tuner Using MATLAB

The main objective of this work is to figure out the key parameters required design of guitar tuners. The purpose of the design is to create an effective logic to detect the basic frequency of a plucked guitar string from its frequency spectrum. A note will be played and whether the pitch needs to go up, go down, or is right will be decided by the program. The algorithm relies on the FFT's expertise, windowing, filterers identification of pitches and their relationship to simple frequency, MATLAB and Si mulink. It can only be tuned to a small number of six distinct notes (E2, A2, D3, G3, B3, E4). These notes on any other instrument, however, are also allowed to be tuned.
Investigating the Relevance of Library Information Science Curriculum to Systems Librarians at Kenya’s Schools of Information Sciences

This study sought to establish the effectiveness of the current curriculum and methodologies used in Schools of information Sciences as a tool to train Systems Librarians in Kenya. It examined the current curricula used by selected Library and Information Schools (LIS) in Kenya, established the content of Information Communication Technology (ICT) courses that are offered in LIS curricula in Kenya, and its relevance to needs. It used a case study of four universities in Kenya that offer LIS education. The study used descriptive case study design to collect data on issues related to LIS curricula as they are at present in the selected universities in Kenya through analysis and comparison curriculum used. In the study, the dependent variable is Systems Librarians while the independent variables include schools of information Sciences in Kenya and LIS curriculum. The study was carried out in selected Universities that teach Library and Information Sciences in Kenya. The study used purposive sampling technique to show accurate, reliable, and valid information from a more representative of all the population who are involved in the developing and implementing the curriculum as well as those who employ the Systems Librarians. The findings show that curricula used by LIS schools in Kenya do not have adequate Information Communication Technology courses to equip those aspiring to be Systems Librarians with adequate skills to carry out their mandate effectively. It shows that there are more general courses that are covered that are not helpful to the students. The study recommends that LIS curricula be reviewed to address the shortcomings noted by increasing the number of ICT courses, benchmarking with universities in developed countries, establish a model LIS for Systems Librarians and standardize curricula for LIS.
Application and Research of Dark Channel Defogging Algorithm in Video Logging Image Enhancement

The video logging technology is applied to detect the general condition of the well. Due to the influence of water fog, the contrast of the video logging image is decreased and the color contrast is distorted. The application and research of the dark channel dehazing algorithm in the video logging image enhancement system is proposed. In this paper, the dark channel defogging algorithm and Retinex defogging algorithm are compared and analyzed respectively, the realization process and application of the two algorithms are described briefly, and the advantages and disadvantages of the two algorithms applied in video logging image enhancement are compared.
Link Bit-Error-Rate Requirement Analysis for Deep Neural Network Accelerators

In convolutional neural network (CNN) accelerators, the dominant power consumption is caused by the access of external data memory. In addition, power and area occupied by I/O interfaces maintaining low bit-error-rate, e.g., 1e-15, grow as the data rate increases. Considering the inherent error resilience of the inference process in machine learning applications, the requirement of error-free communication in the data-path is controversial. In this paper, a custom CNN accelerator integrating a channel emulator is designed by using an FPGA to analyze the effect of the BER of an I/O transceiver on the image classification accuracy. In order to implement a channel emulator, a digital-domain look-up-table (LUT)-based 12-tap FIR filter is employed to create inter-symbol interference (ISI), and a PRBS31 generator is used as a noise source. The implementation was evaluated by running the ImageNet dataset on the FPGA-based custom accelerator (Virtex Ultrascale+) implementing VGG-16. The results show that the BER up to 1e-4 in the memory access has a negligible impact on the inference accuracy.
Determining image age with rank-consistent ordinal classification and object-centered ensemble

A significant number of old photographs including ones that are posted online do not contain the information of the date at which they were taken, or this information needs to be verified. Many of such pictures are either scanned analog photographs or photographs taken using a digital camera with incorrect settings. Estimating the date of such pictures is useful for enhancing data quality and its consistency, improving information retrieval and for other related applications. In this study, we propose a novel approach for automatic estimation of the shooting dates of photographs based on a rank-consistent ordinal classification method for neural networks. We also introduce an ensemble approach that involves object segmentation. We conclude that assuring the rank consistency in the ordinal classification as well as combining models trained on segmented objects improve the results of the age determination task.
Augmented Reality Systems in Gaming and Multimedia

In this paper we discuss about how the Augmented Reality enables us to make games and use the real World as Playground and further use of AR feature in the multimedia area by image Processing and tracking. This paper mainly focusses on how Augmented Reality can improve the World with Gaming and Multimedia with effective utilization of the Augmented detail that are hidden from the view of our naked eye. In this paper we are going to see how we can use some of Augmented Reality technic to implement the use of image tracking process to track objects in Augmented world. We are going to see some example use of the Augmented Reality image of 2D object to Realtime 3D object in Augmented Reality, we have used Unity engine as it the one of the best free and open license Software (open source)to be able to implement our work and demonstrate the use.
Design-Audited Mass Communication Model

Assam is world’s largest tea growing region, even as the tea industry labourers lag behind in human development parameters, suffering high magnitude of malnutrition, infectious diseases, superstition. This research paper tries to understand and analyse the communication process prevalent among them, whether various communication campaigns have effectively served necessary awareness messages to them, whether existing communication models are adequate for them and what design intervention can do for a better tomorrow of such population by exploring the scope of designing a new model. A usability study examines the characteristics of their interaction with media and processing of messages and meanings. The gap in communication message designs indicates the need for a new approach to deal with the unique challenges. An intervention study has been conducted. A new and exclusive model is proposed for them: a model of mass communication to be audited on the principles of design and contextual usability.
Risk Analysis of Station Computer Interlocking in Railway Station

Railway signal system is an important infrastructure to ensure safe operation, loaded safe operation of railway, It has the characteristics of high scientific and technological content and complex security system. The safety of each signal subsystem determines the reliability and safety of the whole system, which takes computer interlocking equipment at ground station as an example, through a comprehensive safety risk analysis of the interlocking system, build a risk matrix based on risk frequency and risk severity, form a risk confidence interval. The constructed risk confidence interval provides a reference method for the safety assessment of the high-speed railway signal system.
Hierarchical Attention Maps for Super Resolution

Abstract Recent state-of-the-art super-resolution methods have achieved impressive performance on ideal datasets. However, these methods always fail in balance between spatial details and high-level perceptual information. Most of them adopt downsampling step to construct Low-Resolution (LR) and High-Resolution (HR) training which may lose local spatial details. To address this issue, we focus on designing a hierarchical attention maps mechanism for recovering both local spatial details and global perceptual information. By using our novel Hierarchical attention module, we can acquire better High-Resolution (HR) predicted images. Finally, we propose a hierarchical multiple scale feature concatenation module aiming at better perception. Extensive experiments on real-world images demonstrate that our method achieved better visual quality both for perception and quantitative estimation.
Customizable Reference Runtime Monitoring of Neural Networks Using Resolution Boxes

Classification neural networks fail to detect inputs that do not fall inside the classes they have been trained for. Runtime monitoring techniques on the neuron activation pattern can be used to detect such inputs. We present an approach for monitoring classification systems via data abstraction. Data abstraction relies on the notion of box with a resolution. Box-based abstraction consists in representing a set of values by its minimal and maximal values in each dimension. We augment boxes with a notion of resolution and define their clustering coverage, which is intuitively a quantitative metric that indicates the abstraction quality. This allows studying the effect of different clustering parameters on the constructed boxes and estimating an interval of sub-optimal parameters. Moreover, we automatically construct monitors that leverage both the correct and incorrect behaviors of a system. This allows checking the size of the monitor abstractions and analysing the separability of the network. Monitors are obtained by combining the sub-monitors of each class of the system placed at some selected layers. Our experiments demonstrate the effectiveness of our clustering coverage estimation and show how to assess the effectiveness and precision of monitors according to the selected clustering parameter and monitored layers.
Improved N - Best Multiple Hypothesis Tracking Algorithm

Abstract In the tracking process of the multi-hypothesis tracking algorithm, many hypothesis are made about the correlation between the track and the measured values. Although its tracking performance is better than other tracking algorithms, it needs a lot of computation time and memory resources when tracking multiple targets or in environments with large clutter due to exponential growth of hypothesis. In view of this situation, an improved N-Best algorithm is proposed to be applied to MHT. The simulation results show that this method can reduce the computation time and realize the track correlation, so it has certain practicability.
Robust cross-lingual knowledge base question answering via knowledge distillation

Purpose Previous knowledge base question answering (KBQA) models only consider the monolingual scenario and cannot be directly extended to the cross-lingual scenario, in which the language of questions and that of knowledge base (KB) are different. Although a machine translation (MT) model can bridge the gap through translating questions to the language of KB, the noises of translated questions could accumulate and further sharply impair the final performance. Therefore, the authors propose a method to improve the robustness of KBQA models in the cross-lingual scenario. Design/methodology/approach The authors propose a knowledge distillation-based robustness enhancement (KDRE) method. Specifically, first a monolingual model (teacher) is trained by ground truth (GT) data. Then to imitate the practical noises, a noise-generating model is designed to inject two types of noise into questions: general noise and translation-aware noise. Finally, the noisy questions are input into the student model. Meanwhile, the student model is jointly trained by GT data and distilled data, which are derived from the teacher when feeding GT questions. Findings The experimental results demonstrate that KDRE can improve the performance of models in the cross-lingual scenario. The performance of each module in KBQA model is improved by KDRE. The knowledge distillation (KD) and noise-generating model in the method can complementarily boost the robustness of models. Originality/value The authors first extend KBQA models from monolingual to cross-lingual scenario. Also, the authors first implement KD for KBQA to develop robust cross-lingual models.
Formation of students' value attitude to a healthy lifestyle on the basis of the concept of pedagogical incentives: a sociocultural analysis

Social and cultural realities of modern society have actualized the increase in the value of a healthy lifestyle, which gives a person some guarantees in a difficult situation of environmental, socio-economic, biotechnological threats. In this regard, one of the priority tasks of the modern education system is preservation and strengthening of the younger generation's health. Students belong to a socially unprotected group. Therefore, the issues of preserving, strengthening and forming a value attitude to health is one of the most important problems of modernity. The education system, being the basis for the stabilization and development of the social structure, must ensure the formation of a personality with certain needs that will contribute to health preservation. Today, the number of students leading a sedentary lifestyle and subject to such bad habits as smoking, drinking alcohol, etc. remains stable. The article discusses the problem of social stimulation of the formation of students' value attitudes towards a healthy lifestyle, analyzes approaches to basic concepts. These include «incentive», «social stimulation», «pedagogical stimulation», «health», «healthy lifestyle». Also, the principles of activity of university teachers on the formation of students' value attitude to a healthy lifestyle have been determined.
Classifying Co-resident Computer Programs Using Information Revealed by Resource Contention

Modern computer architectures are complex, containing numerous components that can unintentionally reveal system operating properties. Defensive security professionals seek to minimize this kind of exposure while adversaries can leverage the data to attain an advantage. This article presents a novel covert interrogator program technique using light-weight sensor programs to target integer, floating point, and memory units within a computer’s architecture to collect data that can be used to match a running program to a known set of programs with up to 100% accuracy under simultaneous multithreading conditions. This technique is applicable to a broad spectrum of architectural components, does not rely on specific vulnerabilities, nor requires elevated privileges. Furthermore, this research demonstrates the technique in a system with operating system containers intended to provide isolation guarantees that limit a user’s ability to observe the activity of other users. In essence, this research exploits observable noise that is present whenever a program executes on a modern computer. This article presents interrogator program design considerations, a machine learning approach to identify models with high classification accuracy, and measures the effectiveness of the approach under a variety of program execution scenarios.
Abstractive Summarization on Dynamically Changing Text

With abundant user data and computing power, using machine learning to automate or minimize our task has become a recent trend. These resources are useful in providing some correlations between data and drawing conclusions. There has been a rapid progression in the field of AI/ML since the past decade with areas like computer vision and natural language processing at the forefront of the race. Text summarization is one of the tasks which has been explored a lot but there hasn’t been any practical application for the same other than tasks such as news or book summarization which are based on Extractive summarization. One of the main disadvantages of Abstractive summarization is there is too much focus on generating good results with respect to a particular sentence and too little on the corpus of text containing thousands of such sentences. There has been some work on multi document summarization but that does not account for the fact that the text can be changed or appended with new text and thus these methods become obsolete. Thus, the proposed method can be applied to a large corpus of data containing thousands of entities/sentences and can also be applied to changing text corpus. The method can be applied to new data that is added separately without rerunning the model on the whole corpus again. This gives us the power of batch processing which can be leveraged according to our space and time constraints. There has not been any research on the same to the best of our knowledge. A transformer model is used to generate individual sentence summaries of respected review text and then used a combination of Universal Sentence Encoder, statistical methods and graph reduction algorithm to select the most relevant sentences to best represent the whole text. By doing so, the same mechanism is applied to incorporate new data that can be added to the corpus and the accuracy would not be affected much. Our results show that even by increasing the degree of contraction of the text corpus (particularly large text corpus), the same accuracy can be achieved.