Reviewerly/exHarmony huggingface dataset:
Performance and Efficiency Evaluation of ASR Inference on the Edge

Automatic speech recognition, a process of converting speech signals to text, has improved a great deal in the past decade thanks to the deep learning based systems. With the latest transformer based models, the recognition accuracy measured as word-error-rate (WER), is even below the human annotator error (4%). However, most of these advanced models run on big servers with large amounts of memory, CPU/GPU resources and have huge carbon footprint. This server based architecture of ASR is not viable in the long run given the inherent lack of privacy for user data, reliability and latency issues of the network connection. On the other hand, on-device ASR (meaning, speech to text conversion on the edge device itself) solutions will fix deep-rooted privacy issues while at same time being more reliable and performant by avoiding network connectivity to the back-end server. On-device ASR can also lead to a more sustainable solution by considering the energy vs. accuracy trade-off and choosing right model for specific use cases/applications of the product. Hence, in this paper we evaluate energy-accuracy trade-off of ASR with a typical transformer based speech recognition model on an edge device. We have run evaluations on Raspberry Pi with an off-the-shelf USB meter for measuring energy consumption. We conclude that, in the case of CPU based ASR inference, the energy consumption grows exponentially as the word error rate improves linearly. Additionally, based on our experiment we deduce that, with PyTorch mobile optimization and quantization, the typical transformer based ASR on edge performs reasonably well in terms of accuracy and latency and comes close to the accuracy of server based inference.
Visualizing GitHub Issues

The rise of distributed version control systems, such as git, and platforms built on top of it, such as GitHub, has triggered a change in how software is developed. Most notably, state-of-the-art practice foresees the use of pull requests and issues, enriched by means to enable discussions among the involved people. Platforms like GitHub and GitLab have thus turned into comprehensive and cohesive modern software development environments, also offering additional mechanisms, such as code review tools and a transversal support for continuous integration and deployment. However, the plethora of concepts, mechanisms, and their interconnections are stored and presented in textual form, which makes the understanding of the underlying evolutionary processes difficult.We introduce the notion of an issue tale, a visual narrative of the events and actors revolving around any GitHub issue, and present an approach, implemented as an interactive visual analytics tool, to depict and analyze the relevant information pertaining to issue tales. We illustrate our approach and its implementation on several open-source software systems.
Memory-Efficient CMSIS-NN with Replacement Strategy

Microcontroller Units (MCUs) are widely used for industrial field applications, and are now ever more being used also for machine learning on the edge, because of their reliability, low cost, and energy efficiency. Due to the MCU resource limitations, the deployed ML models need to be optimized particularly in terms of memory footprint. In this paper, we propose an in-place computation strategy to reduce memory requirements of neural network inference. The strategy exploits the MCU single-core architecture, with sequential execution. Experimental analysis using the CMSIS-NN library on the CIFAE-10 dataset shows that the proposed optimization method can reduce the memory required by a NN model by more than 9%, without impacting the execution performance nor accuracy. The amount of reduction further increases with deeper network architectures.
How Much Do Language Models Copy From Their Training Data? Evaluating Linguistic Novelty in Text Generation Using RAVEN

Abstract Current language models can generate high-quality text. Are they simply copying text they have seen before, or have they learned generalizable linguistic abstractions? To tease apart these possibilities, we introduce RAVEN, a suite of analyses for assessing the novelty of generated text, focusing on sequential structure (n-grams) and syntactic structure. We apply these analyses to four neural language models trained on English (an LSTM, a Transformer, Transformer-XL, and GPT-2). For local structure—e.g., individual dependencies—text generated with a standard sampling scheme is substantially less novel than our baseline of human-generated text from each model’s test set. For larger-scale structure—e.g., overall sentence structure—model-generated text is as novel or even more novel than the human-generated baseline, but models still sometimes copy substantially, in some cases duplicating passages over 1,000 words long from the training set. We also perform extensive manual analysis, finding evidence that GPT-2 uses both compositional and analogical generalization mechanisms and showing that GPT-2’s novel text is usually well-formed morphologically and syntactically but has reasonably frequent semantic issues (e.g., being self-contradictory).
Emerging investigator series: quantifying the impact of cloud cover on solar irradiance and environmental photodegradation

Environmental photodegradation is dependent on the solar irradiance that reaches the Earth's surface, and photodegradation half-lives of contaminants are typically estimated assuming clear sky (i.e., cloudless) conditions. In this work, the effect of cloud cover on solar irradiance was investigated. Data from the National Renewable Energy Laboratory (NREL), which spanned 3 years of observations (10/2017 to 12/2020), were used to train two machine learning models to predict irradiance based on three inputs - day of year, time of day, and percentage of the sky that was cloudy. Results showed a non-linear relationship between cloud cover and irradiance. Solar irradiance was minimally impacted up to ≈50% cloud cover but decreased by ≈67% at 100% cloud cover. Both random forest and artificial neural network models performed well with relative root mean squared errors of 26-31%, which varied depending on the source of cloud cover data and the spectral region being modeled. Daily irradiance values for a whole year were predicted for varying cloud conditions using the machine learning models; this result was approximated using a quadratic fit of y = 1 - 0.00243x - (4.24 × 10-5)x2 where y is the fraction of clear sky irradiance expected and x is the percentage of cloud cover in the sky. In addition, the model results supported that there was no wavelength dependence for the effect of cloud cover. Therefore, decreases in both direct and indirect photodegradation rates should be proportional to the decrease in irradiance, which has a non-linear dependence on cloud cover.
Aiding IoT and Cloud to Control COVID-19: A Systematic Approach

IoT is an assembly of devices having different capacities. These devices are provided with a common platform to communicate with each other. These devices are unsupervised and hence require more security compared to connected devices. The proposal aims to provide reliability by applying blockchain. It also enhances the work by providing better scalability to information and its availability. A wide variety of users will be able to access information over the cloud by authenticating itself. The work increases reliability by 3.1% and makes 75% more data available compared to previous contribution.
Social Engineering: The Art of Attacks

The correct management of information systems security is often overlooked in technological measures and management efforts, and although there are now many tools to address security threats, the human aspect has been neglected. This paper discusses the human factors that could potentially lead to intrusions with social engineering. Social engineering is a method used by hackers to obtain access to systems by manipulating flaws in behavior known as mental preconceptions. Social engineering is a risk to security information and must be considered just as important as in technological areas. In this paper we also approach social engineering, taking an introductory brief in its history, what is psychological manipulation and human weaknesses, what are the social engineering attacks, how they use authority and fear establishment, it is also approached how a social engineering attack is executed, providing value monetizing the scam, and identity exploration.
COVID-19 Wave Pattern Analysis: An Exhaustive Survey

COVID-19 is one of the severe problems in front of the world. The world is facing a second and third wave now. Different researchers are planning the pattern of spread based on wave one behavior, vaccination patterns, medical facilities, etc. This paper focuses on the main models for understanding the patterns discussed and valuable for understanding and addressing the COVID-19 issue. COVID-19 is a highly contagious virus that spreads very quickly from person to person. This virus has gripped the world even though many countries like the UK and Thailand have managed to contain its spread significantly. The first case of this virus was reported in India in March 2019. Almost all countries had to suffer human losses due to this virus. Countries are not prepared to face the consequences arising from this pandemic. The study found that social distance, geographic location, and temperature, among other parameters, could allow researchers to develop more accurate models. When the pandemic started to spread, the researchers' focus was on how to control the pandemic from spreading to other areas. When the pandemic began to slow down, the emphasis was on developing models related to recovery and the long-term impact caused by the pandemic.
Generating Optimal Attack Paths in Generative Adversarial Phishing

Phishing attacks have witnessed a rapid increase thanks to the matured social engineering techniques, COVID-19 pandemic, and recently adversarial deep learning techniques. Even though adversarial phishing attacks are recent, attackers are crafting such attacks by considering context, testing different attack paths, then selecting paths that can evade machine learning phishing detectors. This research proposes an approach that generates adversarial phishing attacks by finding optimal subsets of features that lead to higher evasion rate. We used feature engineering techniques such as Recursive Feature Elimination, Lasso, and Cancel Out to generate then test attack vectors that have higher potential to evade phishing detectors. We tested the evasion performance of each technique then classified different evasion tests as passed or failed depending on their evasion rate. Our findings showed that our threat model has better evasion capability compared to the original Generative Adversarial Deep Neural Network (GAN) which perturbs features in a random manner.
DG‐based SPO tuple recognition using self‐attention M‐Bi‐LSTM

This study proposes a dependency grammar-based self-attention multilayered bidirectional long short-term memory (DG-M-Bi-LSTM) model for subject–predicate–object (SPO) tuple recognition from natural language (NL) sentences. To add recent knowledge to the knowledge base autonomously, it is essential to extract knowledge from numerous NL data. Therefore, this study proposes a high-accuracy SPO tuple recognition model that requires a small amount of learning data to extract knowledge from NL sentences. The accuracy of SPO tuple recognition using DG-M-Bi-LSTM is compared with that using NL-based self-attention multilayered bidirectional LSTM, DG-based bidirectional encoder representations from transformers (BERT), and NL-based BERT to evaluate its effectiveness. The DG-M-Bi-LSTM model achieves the best results in terms of recognition accuracy for extracting SPO tuples from NL sentences even if it has fewer deep neural network (DNN) parameters than BERT. In particular, its accuracy is better than that of BERT when the learning data are limited. Additionally, its pretrained DNN parameters can be applied to other domains because it learns the structural relations in NL sentences.
Artificial Intelligence and Deep Learning for Weapon Identification in Security Systems

Abstract As crime rates rise at large events and possibly lonely places, security is always a top concern in every field. A wide range of issues may be solved with the use of computer vision, including anomalous detection and monitoring. Intelligence monitoring is becoming more dependent on video surveillance systems that can recognise and analyse scene and anomaly occurrences. Using SSD and Faster RCNN techniques, this paper provides automated gun (or weapon) identification. Use of two different kinds of datasets is included in the proposed approach. As opposed to the first dataset, the second one comprises pictures that have been manually tagged. However, the trade-off between speed and precision in real-world situations determines whether or not each method will be useful.
Dynamic path planning of USV with towed safety boundary in complex ocean environment

In order to solve the problem of USV dynamic path planning with towed safety boundary in complex marine environment, an adaptive USV dynamic path planning algorithm is proposed. Firstly, the obstacle avoidance problem of the USV with the towed safety boundary and the bounded tension problem of the cable array are analyzed respectively, and then correspondent safety boundary model and tension boundary condition are established. Secondly, a fuzzy mechanism strategy is developed to automatically choose an appropriate local searching radius according to the degree of the complex obstacles. Moreover, for some constraints such as large-angle turn and path demand in the process of path planning, the USV perspective self-adjusting mechanism and smooth optimization schedule of the path are designed to make it more feasible. Finally, simulation results show that the proposed algorithm can enable the USV with towed safety boundary to carry out dynamic path planning in the complex ocean environment effectively.
Zero-shot semantic segmentation via spatial and multi-scale aware visual class embedding

Fully supervised semantic segmentation technologies bring a paradigm shift in scene understanding. However, the burden of expensive labeling cost remains as a challenge. To solve the cost problem, recent studies proposed language model based zero-shot semantic segmentation (L-ZSSS) approaches. In this paper, we address L-ZSSS has a limitation in generalization which is a virtue of zero-shot learning. Tackling the limitation, we propose a language-model-free zero-shot semantic segmentation framework, Spatial and Multi-scale aware Visual Class Embedding Network (SM-VCENet). Furthermore, leveraging vision-oriented class embedding SM-VCENet enriches visual information of the class embedding by multi-scale attention and spatial attention. We also propose a novel benchmark (PASCAL2COCO) for zero-shot semantic segmentation, which provides generalization evaluation by domain adaptation and contains visually challenging samples. In experiments, our SM-VCENet outperforms zero-shot semantic segmentation state-of-the-art by a significant margin in both PASCAL-5i and PASCAL2COCO benchmarks.
A GUI based application for Low Intensity Object Classification &amp; Count using SVM Approach

There is a requirement of processing low intensity images from EO (Electro Optical), IR (Infra-Red) and ISAR (Inverse Synthetic Aperture Radar) sensors on airborne platforms to detect and classify targets(ships, vessels, other objects) against a library of images in real time with a degree of confidence. Pre-processing of the test input images improves the accuracy of detection and classification. The proposed method verifies and validates the trained software against the pre-processed images. The objective of the proposed approach is to train the machine learning technique in order to estimate the efficiency and accuracy of the classified and detected output from the Deep leaning models. In our work, we have compared the results in terms of accuracy and time with the previous researchers work and we have summarized about our method gives better accuracy.
Level of Income and the Investment Development Path Theory: Evidence From Africa

The investment development path theory delineates countries in stages I, II, III, IV, and V according to the level of development as developing, transition, and developed countries. The World Bank’s classification of countries by income, however, identifies countries as lower-income, lower-middle-income, upper-middle-income, and high income. In this paper, we test the investment development path theory for countries based on the level of income using data from 1980 to 2019. Africa offers unique data as it is the only continent made up of entirely developing countries together with countries that fit into all four classifications based on income. Income level classifications appear to enhance the position of countries within the investment development path ahead of that based on the United Nations classification. The use of income classification should complement that of the United Nations in the empirical testing of the investment development path theory for a more current investment development path status.
Image reconstruction attacks on distributed machine learning models

Recent developments in Deep Neural Networks have resulted in their wide deployment for services around many aspects of human life, including security critical domains that handle sensitive data. Congruently, we have seen a proliferation of IoT devices with limited resources. Together, these two trends have led to the distribution of data analysis, processing, and decision making between edge devices and third parties such as cloud services. In this work we assess the security of the previously proposed distributed machine learning (ML) schemes by analyzing the information leaked from the output of the edge devices, i.e. the intermediate representation (IR). We particularly look at a Deep Neural Network that is used for video/image classification and tackle the problem of image/frame reconstruction from the output of the edge device. Our work focuses on assessing whether the proposed scheme of partitioned enclave execution is secure against chosen-image attacks (CIA). Given the attacker has the capability of querying the model under attack (victim model) to create image-IR pairs, can the attacker reconstruct the private input images? In this work we show that it is possible to carry out a black-box reconstruction attack by training a CNN based encoder-decoder architecture (reconstruction model) using image-IR pairs. Our tests show that the proposed reconstruction model achieves a 70% similarity between the original image and the reconstructed image.
Financial Determinants of Informal Financial Development in Sub-Saharan Africa

This study assesses financial determinants of informal financial sector development in 48 Sub-Saharan African countries for the period 1995-2017. Quantile regressions are used as the empirical strategy which enables the study to assess the determinants throughout the conditional distribution of informal sector development dynamics. The following financial determinants affect informal financial development and financial informalization differently in terms of magnitude and sign: bank overhead costs; net internet margin; bank concentration; return on equity; bank cost to income ratio; financial stability; loans from non-resident banks; offshore bank deposits and remittances. The determinants are presented from a plethora of perspectives, inter alia: U-Shape, S-Shape and positive or negative thresholds. The study not only provides a practical way by which to assess the incidence of financial determinants on informal financial sector development, but also provides financial instruments by which informal financial development can be curbed.
Fine-Context Shadow Detection using Shadow Removal

Current shadow detection methods perform poorly when detecting shadow regions that are small, unclear or have blurry edges. In this work, we attempt to address this problem on two fronts. First, we propose a Fine Context-aware Shadow Detection Network (FCSD- Net), where we constraint the receptive field size and focus on low-level features to learn fine context features better. Second, we propose a new learning strategy, called Restore to Detect (R2D), where we show that when a deep neural network is trained for restoration (shadow removal), it learns meaningful features to delineate the shadow masks as well. To make use of this complementary nature of shadow detection and removal tasks, we train an auxiliary network for shadow removal and propose a complementary feature learning block (CFL) to learn and fuse meaningful features from shadow removal network to the shadow detection network. We train the proposed network, FCSD-Net, using the R2D learning strategy across multiple datasets. Experimental results on three public shadow detection datasets (ISTD, SBU and UCF) show that our method improves the shadow detection performance while being able to detect fine context better compared to the other recent methods. Our proposed learning strategy can also be adopted easily as a useful pipeline in future advances in shadow detection and removal.
Implementation K-Means Clustering Method in Job Recommendation System

Work is important for everyone to earn income. With the large number of new graduates each year, finding job vacancies is a problem for students who have just completed their studies in higher education because they still do not have work experience so they are required to look for jobs that really match their criteria. Applications made can recommend specific job vacancies for undergraduates from universities (undergraduates) with the K-Means Clustering method. Applications in the form of websites that become third parties for companies and applicants. This application is one of the means that can provide solutions to companies and applicants in finding workers or jobs using a recommendation system. The problem to be studied is how to apply the K-Means Clustering method to the job vacancy recommendation system. The recommendation system in this application will calculate the level of match of the applicant’s main skills, salary, location, and other skills with the needs of the company. The stages of making a recommendation system are making system designs and designs which include context diagrams, DFD, ERD and interface design. built with PHP, Java, jQuery, JavaScript, HTML, and CSS. Program testing is done by black box testing method. The weight given shows that the K-Means Clustering method can be applied to the job vacancy recommendation system and can display job recommendations according to the applicant’s personal data. Questionnaire testing is given to applicants, companies, and admins with elements of testing based on user satisfaction, user convenience and system quality, resulting in the conclusion that the system can run well by getting a percentage of 87.6%.