Reviewerly/exHarmony huggingface dataset:
Edge-aware salient object detection network via context guidance

Fully convolutional network (FCN) based salient object detection methods have shown their advantages in highlighting salient regions because they can obtain global semantic information. And the high-level semantics are usually passed in a top-down pathway. However, the semantic information would be diluted progressively among different level features. To alleviate this issue, we propose a novel edge-aware salient object detection network. Our network utilizes high-level semantic information to assist the feature selection of shallower layers. Specifically, we extract refined features from different levels of the backbone. Then, we obtain global contextual information to locate the salient objects by extracting multi-scale features and emphasizing the important feature channels. In order to assist the shallower layers to pay attention to the learning of meaningful local information, we adopt a context guidance strategy to fuse the high-level and low-level information. Finally, we supervise the generation of low-level edge information to preserve the salient object boundaries. Extensive experiments demonstrate that the proposed mode performs favorably against most state-of-the-art methods under different evaluation metrics on six popular benchmarks.
Intelligent Anomaly Detection Model For Atm Booth Surveillance Using Machine Learning Algorithm : Intelligent ATM Survillance Model

Automated Teller Machines (ATM) is an essential and inevitable part of our daily life, the money transactions become safe and fast with the help of ATMs, still, the machines and the ATM booths are prone to the severe criminal activities. In any case of criminal activity, the victims will be helpless when the booths are located in remote areas. According to the current scenario, the investigation officials will be aware of the crime activity only after the crime is over and they will try to track the criminals using the recorded data in surveillance cameras. The proposed study is aimed to identify the abnormal activity in ATM Booths with the help of artificial intelligent techniques. The Support Vector Machine (SVM) strategy is employed to classify the activity into normal and abnormal class, the continuous video streams are encoded by the histogram of gradients technique, and the feature mapping is made by K-means clustering algorithm. The parameters of the SVM classifier algorithm is tuned by the proposed hybrid HGOA optimization algorithm. The performance of the proposed model is evaluated for human motion identification and the anomaly event is identified on two benchmark datasets such as HMDB-51 and CAVIAR datasets.
Fusion of ASTER satellite imagery, geochemical and geology data for gold prospecting in the Astaneh granite intrusive, West Central Iran

In this study, ASTER imagery, geochemical, lithological, and structural data are exploited for Mineral Potential Mapping (MPM) of the Astaneh granitic pluton and its surrounding area. The independent component analysis (ICA) and Matched Filtering (MF) techniques are applied to ASTER data for detecting alteration mineral assemblages. Sericitically argillically altered minerals associated with jarosite and chlorite/epidote are mapped using the ICA technique. MF fraction images derived from n-dimensional visualisation (n-DV) tool facilitated detecting goethite, haematite, limonite, muscovite, kaolinite, illite, chlorite and epidote associated with gold occurrences. The distribution of Cu, Pb, Zn and Au is considered for generating geochemical anomaly layers. Strong Cu, Zn and Au anomalies are found to be associated with gold mineralisation. The lithological units hosting gold mineralisation and intersection of NE–SW and NW–SE trending lineaments are also considered. Fuzzy Logic Model (FLM) was used to generate gold prospectivity map for the study area by fusing the alteration, geochemical, geology and structural layers. Several high prospective zones are identified in the central and southeastern part of the study area. A majority of delineated exploration targets are either linked to the plutonic body or its surrounding metamorphic rocks. This study demonstrated a viable approach for future gold prospecting in the study area.
Cooperative Decision Making of Supply Chain Members of Shipping Logistics Services Under the Background of Blockchain

The supply chain of shipping logistics services is an important branch of logistics service supply chain. To ensure the effective operation of the supply chain and to solve problems such as asymmetric information, difficult data quality assurance, and uncontrollable transportation in shipping logistics services, blockchain technology are proposed to reduce information interaction problems. In view of the factors of information sharing and customers’ sensitivity to information quality, a tripartite evolutionary game model with the shipping company, the port and the freight forwarder as the research objects was established, and the cooperative decision making of node companies was discussed before and after the application of blockchain technology. Theoretical derivation and data analysis show that ports and freight forwarder in the supply chain of shipping logistics services dominated by shipping companies are less affected by information sharing and customers’ sensitivity to information quality. With the increase in the application of blockchain technology, when customers have lower expectations for information quality, shipping companies are more willing to cooperate actively. The willingness to cooperate actively between ports and shipping companies is increasing faster than when blockchain technology is not used. Therefore, shipping companies should encourage the active use of blockchain technology to reduce the degree of information sharing between ports and freight forwarder and the influences of customer expectations on shipping companies.
Challenges and opportunities for wearable IoT forensics: TomTom Spark 3 as a case study

Wearable IoT devices like fitness trackers and smartwatches continue to create opportunities and challenges for forensic investigators in the acquisition and analysis of evidential artefacts in scenarios where such devices are a witness to a crime. However, current commercial and traditional forensic tools available to forensic investigators fall short of conducting device extraction and analysis of forensic artefacts from many IoT devices due to their heterogeneous nature. In this paper, we conduct a comprehensive forensic analysis and show artefacts of forensic value from the physical TomTom Spark 3 GPS fitness smartwatch, its companion app installed on an Android smartphone, and Bluetooth event logs located in the app's metadata. Our forensic methodology and analysis involved the combination and use of a non-forensic tool, a commercial forensic tool, and a non-forensic manufacturer-independent analysis platform tool specifically designed for endurance athletes to identify, extract, analyse, and reconstruct user activity data in an investigative scenario. We show forensic metadata associated with the device information, past user activities, and audio files from the physical smartwatch. We recovered data associated with past user activities stored in proprietary activity files and databases maintained by the app on an Android smartphone. From the event logs, we show when user activity was synced with the app and uploaded to the device cloud storage. The results from our work provide vital references for forensic investigators to aid criminal investigations, highlight limitations of current forensic tools, and for developers of forensic tools an incentive into developing forensic software applications and tools that can decode all relevant data generated by wearable IoT devices.
Uncertainty principle for communication compression in distributed and federated learning and the search for an optimal compressor

In order to mitigate the high communication cost in distributed and federated learning, various vector compression schemes, such as quantization, sparsification and dithering, have become very popular. In designing a compression method, one aims to communicate as few bits as possible, which minimizes the cost per communication round, while at the same time attempting to impart as little distortion (variance) to the communicated messages as possible, which minimizes the adverse effect of the compression on the overall number of communication rounds. However, intuitively, these two goals are fundamentally in conflict: the more compression we allow, the more distorted the messages become. We formalize this intuition and prove an {\em uncertainty principle} for randomized compression operators, thus quantifying this limitation mathematically, and {\em effectively providing asymptotically tight lower bounds on what might be achievable with communication compression}. Motivated by these developments, we call for the search for the optimal compression operator. In an attempt to take a first step in this direction, we consider an unbiased compression method inspired by the Kashin representation of vectors, which we call {\em Kashin compression (KC)}. In contrast to all previously proposed compression mechanisms, KC enjoys a {\em dimension independent} variance bound for which we derive an explicit formula even in the regime when only a few bits need to be communicate per each vector entry.
CineScale: A dataset of cinematic shot scale in movies

We provide a database containing shot scale annotations (i.e., the apparent distance of the camera from the subject of a filmed scene) for more than 792,000 image frames. Frames belong to 124 full movies from the entire filmographies by 6 important directors: Martin Scorsese, Jean-Luc Godard, Béla Tarr, Federico Fellini, Michelangelo Antonioni, and Ingmar Bergman. Each frame, extracted from videos at 1 frame per second, is annotated on the following scale categories: Extreme Close Up (ECU), Close Up (CU), Medium Close Up (MCU), Medium Shot (MS), Medium Long Shot (MLS), Long Shot (LS), Extreme Long Shot (ELS), Foreground Shot (FS), and Insert Shots (IS). Two independent coders annotated all frames from the 124 movies, whilst a third one checked their coding and made decisions in cases of disagreement. The CineScale database enables AI-driven interpretation of shot scale data and opens to a large set of research activities related to the automatic visual analysis of cinematic material, such as the automatic recognition of the director’s style, or the unfolding of the relationship between shot scale and the viewers’ emotional experience. To these purposes, we also provide the model and the code for building a Convolutional Neural Network (CNN) architecture for automated shot scale recognition. All this material is provided through the project website, where video frames can also be requested to authors, for research purposes under fair use.
An Application of Deep-Learning Techniques to Face Mask Detection During the COVID-19 Pandemic

COVID-19 is an infectious disease that is primarily spread by respiratory droplets of infected patients. Their droplets are generated while they speak, sneeze, or cough. In Thailand, wearing a face mask seems a must when you travel and go around to other places. The policy of wearing a face mask is applied in almost everywhere both indoor and outdoor, especially indoor places. You will be not allowed to enter the places without wearing a mask. In this paper, we developed a convolutional neural network and trained it with the data set of 848 images with resolution of 416x416 pixels. The accuracy rate is 81% based on the performance of the trained model. We implemented it successfully and installed the system (both hardware and software) in a real environment, i.e., in a front gate of a campus building during an open-house event.
Emerging and Established Trends to Support Secure Health Information Exchange

This work aims to provide information, guidelines, established practices and standards, and an extensive evaluation on new and promising technologies for the implementation of a secure information sharing platform for health-related data. We focus strictly on the technical aspects and specifically on the sharing of health information, studying innovative techniques for secure information sharing within the health-care domain, and we describe our solution and evaluate the use of blockchain methodologically for integrating within our implementation. To do so, we analyze health information sharing within the concept of the PANACEA project that facilitates the design, implementation, and deployment of a relevant platform. The research presented in this paper provides evidence and argumentation toward advanced and novel implementation strategies for a state-of-the-art information sharing environment; a description of high-level requirements for the transfer of data between different health-care organizations or cross-border; technologies to support the secure interconnectivity and trust between information technology (IT) systems participating in a sharing-data "community"; standards, guidelines, and interoperability specifications for implementing a common understanding and integration in the sharing of clinical information; and the use of cloud computing and prospectively more advanced technologies such as blockchain. The technologies described and the possible implementation approaches are presented in the design of an innovative secure information sharing platform in the health-care domain.
Physical security of deep learning on edge devices: Comprehensive evaluation of fault injection attack vectors

Decision making tasks carried out by the usage of deep neural networks are successfully taking over in many areas, including those that are security critical, such as healthcare, transportation, smart grids, where intentional and unintentional failures can be disastrous. Edge computing systems are becoming ubiquitous nowadays, often serving deep learning tasks that do not need to be sent over to servers. Therefore, there is a necessity to evaluate the potential attacks that can target deep learning in the edge. In this work, we present evaluation of deep neural networks (DNNs) reliability against fault injection attacks. We first experimentally evaluate DNNs implemented in an embedded device by using laser fault injection to get the insight on possible attack vectors. We show practical results on four activation functions, ReLu, softmax, sigmoid, and tanh. We then perform a deep study on DNNs based on derived fault models by using several different attack strategies based on random faults. We also investigate a powerful attacker who can find effective fault location based on genetic algorithm, to show the most efficient attacks in terms of misclassification success rates. Finally, we show how a state of the art countermeasure against model extraction attack can be bypassed with a fault attack. Our results can serve as a basis to outline the susceptibility of DNNs to physical attacks which can be considered a viable attack vector whenever a device is deployed in hostile environment.
Key Qualities of Conversational Chatbots – the PEACE Model

Open-domain chatbots engage in natural conversations with the user to socialize and establish bonds. However, designing and developing an effective open-domain chatbot is challenging. It is unclear what qualities of such chatbots most correspond to users’ expectations. Even though existing work has considered a wide range of aspects, some key components are still missing. More importantly, the consistency and validity of the combined criteria have not been tested. In this paper, we describe a large-scale survey using a consolidated model to elicit users’ preferences, expectations, and concerns. We apply structural equation modeling methods to further validate the data collected from the user survey. The outcome supports the consistency, validity, and reliability of the model, which we call PEACE (Politeness, Entertainment, Attentive Curiosity, and Empathy). PEACE, therefore, defines the key determinants most predictive of user acceptance. This has allowed us to develop a set of implications useful for the development of compelling open-domain chatbots.
Implementation of Least Significant Bit Image Steganography with Advanced Encryption Standard

Steganography is the method of hiding messages or data (plain text) by encoding them into some other data (usually images). Through this mechanism, the secured plain text message can be completely concealed from unauthorized access. Using steganography, we can hide messages by encoding it in an image and then send it to the receiving user. In our proposed method using steganography and encryption, we have implemented a mechanism to transmit a message from the sender to the receiver and increase the security of the transmission while also providing confidentiality. The receiver will receive both the encoded image and the key with why the encrypted method can be decrypted. We will be using the Least Significant Bit (LSB) method of steganography and AES encryption to achieve this.
Real-time Trajectory Generation for Multiple Drones using Bézier Curves

Practical applications of drones are expanding into many new areas due to their fast-evolving technology. Looking further into the future, it is very likely that applications will require more than one drone to tackle a specific task, calling for reliable and efficient algorithms that can generate collision-free trajectories for multiple drones, under timing constraints of real-time applications. In this paper, we study a motion planning method based on the Bézier parametrization of spatial paths with a special focus on the less addressed issue in this method, (inefficient) constraint evaluation, that might hinder its use in real-time trajectory generation for multi-drone applications. We take advantage of the Bézier curves properties to obtain a small-scale optimization problem and find a finite set of inequalities that guarantee constraints satisfaction. We also propose a method to lower the conservatism in the resulting set of inequalities without the need to use unnecessary high-degree Bézier curves. Numerical results illustrate the efficacy of the presented method in reducing the computational costs associated with generating collision-free trajectories for multiple drones and re-planning them online with a receding horizon.
Low Resource Neural Machine Translation from English to Khasi: A Transformer-Based Approach

With the increased amount of information that is readily available in digital platforms, the demand and significance of machine translation (MT) between languages have also risen. MT can bridge this gap where the need for a professional translator can be minimized. This paper focuses on low resource machine translation from English to Khasi and vice-versa which was encouraged from the recent development of the cross-lingual language model pretraining system developed by Facebook AI Research. The approach is well suited for a low resource language and the translation model was trained using a combination of monolingual and parallel corpus. When tested using a similar domain test corpus, a Blue score of 39.63 and 32.69 were achieved for translating English–Khasi and Khasi–English respectively. However, while testing by using a general test domain corpus, a Blue scores of 5.33 and 2.86 for translating English–Khasi and Khasi–English were obtained respectively. Considering the minimal corpus size, the technique shows promising results, especially when low resource language is considered.
Does semantics aid syntax? An empirical study on named entity recognition and classification

Many researchers jointly model multiple linguistic tasks (e.g., joint modeling of named entity recognition and named entity classification and joint modeling of syntactic parsing and semantic parsing) with an implicit assumption that these individual tasks can enhance each other via the joint modeling. Before conducting research on jointly modeling multiple tasks, however, such researchers hardly examine whether such assumption is true or not. In this paper, we empirically examine whether named entity classification improves the performance of named entity recognition as an empirical case of examining whether semantics improves the performance of a syntactic task. To this end, we firstly specify the way to determine whether a linguistic task is a syntactic task or a semantic task according to both syntactic theory and semantic theory. After that, we design and conduct extensive experiments on two well-known benchmark datasets using three representative yet diverse state-of-the-art models. Experimental results demonstrate that named entity recognition does not lie at the semantic level and is not a semantic task; instead, it is a syntactic task and that the joint modeling of named entity recognition and classification does not improve the performance of named entity recognition. Experimental results also demonstrate that traditional handcrafted feature models can achieve state-of-the-art performance in comparison with the auto-learned feature model on named entity recognition.
A Comparative Investigation of Consensus Algorithms in Collaboration with IoT and Blockchain

One of the large and emerging technologies nowadays is blockchain which has the impending potential to renovate the approach of contributing the large amount of data and information along with that it is also boosting confidence among the users. The drastic expansion in data creates security, privacy with assurance issues in the period of the Internet. The proposal is built to examine the nitty-gritty of blockchain technology and consensus algorithms also with the assessment of consensus algorithms and their area of relevance. With the appearance of Internet of Things (IoT), the enormous significant information is obtainable from the Internet. For doing cryptographic analysis and hashing procedures. This is a kind of challenge for the researchers to provide secure data without involving a third-party intervention for central environment. We can also reduce the transmission announcement expenditure of consensus for connected nodes with the assistance of restricted remembrance.
A New Big Data Feature Selection Approach for Text Classification

Feature selection (FS) is a fundamental task for text classification problems. Text feature selection aims to represent documents using the most relevant features. This process can reduce the size of datasets and improve the performance of the machine learning algorithms. Many researchers have focused on elaborating efficient FS techniques. However, most of the proposed approaches are evaluated for small datasets and validated using single machines. As textual data dimensionality becomes higher, traditional FS methods must be improved and parallelized to handle textual big data. This paper proposes a distributed approach for feature selection based on mutual information (MI) method, which is widely applied in pattern recognition and machine learning. A drawback of MI is that it ignores the frequency of the terms during the selection of features. The proposal introduces a distributed FS method, namely, Maximum Term Frequency-Mutual Information (MTF-MI), based on term frequency and mutual information techniques to improve the quality of the selected features. The proposed approach is implemented on Hadoop using the MapReduce programming model. The effectiveness of MTF-MI is demonstrated through several text classification experiments using the multinomial Naïve Bayes classifier on three datasets. Through a series of tests, the results reveal that the proposed MTF-MI method improves the classification results compared with four state-of-the-art methods in terms of macro-F1 and micro-F1 measures.