Reviewerly/exHarmony huggingface dataset:
Accelerating Video Object Detection by Exploiting Prior Object Locations

We provide a set of generic modifications to improve the execution efficiency of single-shot object detectors by exploiting prior object locations in video sequences. We propose a crop-based method to accelerate object detection tasks. It dynamically generates crop regions based on prior information and exploits scene sparsity enabling focused use of computational resources. In contrast to prior work, smaller input resolutions for processing crop regions are used to further reduce computational load. The execution efficiency is increased by avoiding multiple executions of the detector in full resolution. Data augmentations are used to successfully train these lower-resolution networks and maintain their accuracy at the baseline level while reducing inference time. Experiments with two public datasets, UA-DETRAC [13] and UAVDT [2], using the SSD-ML [19] object detection architecture with $$128\times 128$$ , $$64\times 64$$ and $$32\times 32$$ input resolutions show that we can achieve a maximum speedup by a factor of 1.7 on the UA-DETRAC dataset, and 1.6 on the UAVDT dataset while delivering the same level of accuracy as the base method. An extensive set of experiments demonstrates the speed-accuracy trade-off and shows that our method can achieve accuracy comparable to state-of-the-art methods at lower execution time.
Low-Rank Softmax Can Have Unargmaxable Classes in Theory but Rarely in Practice

Classifiers in natural language processing (NLP) often have a large number of output classes.For example, neural language models (LMs) and machine translation (MT) models both predict tokens from a vocabulary of thousands.The Softmax output layer of these models typically receives as input a dense feature representation, which has much lower dimensionality than the output.In theory, the result is some words may be impossible to be predicted via argmax, irrespective of input features, and empirically, there is evidence this happens in small language models (Demeter et al., 2020).In this paper we ask whether it can happen in practical large language models and translation models.To do so, we develop algorithms to detect such unargmaxable tokens in public models.We find that 13 out of 150 models do indeed have such tokens; however, they are very infrequent and unlikely to impact model quality.We release our algorithms and code so that others can test their models. 1
Emerging Scientific Topic Discovery by Finding Infrequent Synonymous Biterms

With the increasing information load brought by the accelerated growth of research papers, the automatic discovery of a field’s emerging scientific topics becomes vital. It enables broad applications, such as optimizing resource allocations for promising research areas, predicting future technology trends, finding knowledge gaps and new concepts, and recommending personalized research directions. However, two challenges - the rareness of emerging-topic publications and the linguistic diversity in the description of emerging topics - hinder existing text analytic methods from effectively identifying the evolving terms in emerging topics. According to our observation, an emerging topic originating from a collaboration of two sub-fields could be represented by a biterm, each term from one sub-field. In this paper, we propose a novel finding Infrequent Synonymous Biterms to discover Emerging Scientific Topics (isBEST) method to overcome the challenges. Our isBEST method reduces linguistic diversity using document-level clustering to find the linguistic variants of each key biterm. The biterms in the same cluster expressing very similar meanings are unified to the most common synonymous biterm. Then, to address the rareness issue, isBEST converts each input document into a vector of coefficients on synonymous biterms and clusters them at the corpus level with cosine similarity. In each document, larger coefficients are assigned to rarer synonymous biterms. The underlying logic is the higher chance of a rarer synonymous biterm to be an emerging topic denoted by the two terms, each from a collaborating sub-field. Experiments on two large scholarly paper datasets demonstrate the accuracy and effectiveness of our isBEST method.
Improving Drift Detection by Monitoring Shapley Loss Values

Along the deployment of Machine Learning models rises an inherent need for monitoring, where model performances should be tracked as well as potential drifts. In a live environment, with evolving data, the risk is for the model to become ill-adapted for the given situation. The failure to detect drift while leading to a performance deterioration could also cause side effects due to model over-trust. Informing the user of any anomaly upon detection is the key to enabling any action. We propose Shap-ADWIN, a novel approach improving the performance of state-of-the-art drift detectors such as ADWIN by leveraging the information brought by Shapley Loss Values. While common practice is to monitor the evolution of the loss of models at most for every predicted instance, the proposed solution monitors each individual instance and features the Shapley Loss value. Whenever the loss is attributed more toward a given feature the information becomes more contrasted, which enables a better detection. Indeed the signal-to-noise ratio is higher on that feature and allows the detector to leverage that information. The opposite case being equal Shapley values that are just the Loss under-scaled for every feature. Moreover, noise over the output would be equally distributed along with each Shapley Loss value of every feature providing lower information to noise ratio and allows a more reliable detection. We provide: a restricted proof, experiments and source code. Results were obtained using synthetically generated data presenting diverse types of drift, showing the performance of Shap-ADWIN over ADWIN.
Establishment of a Situation Awareness Analysis Model for Flight Crew Alerting

Flight crew should maintain a sufficient situation awareness during flight especially during abnormal and emergency situation. In order to analyze the situation awareness requirement, this study combines the GDTA method proposed by Endsley (goal-oriented task analysis) and FCM (fuzzy cognitive map) to establish a situation awareness model. It is more scientific to obtain the critical factors related to situation awareness in the process of handling the flight crew alerting.
Validate Merchant Server for Secure Payment Using Key Distribution

Nowadays, growing and competitiveness of a financial sector and e-healthcare application developed rapidly and quicker ever so that mobile payment plays a vital role to make easier and quicker. In the existing paper, the cloud transmits the hash value straight to the merchant server without checking for fraud. This paper aims to provide a secure authentication mechanism between the cloud and the merchant server to avoid fraudulent merchant servers. For trustworthiness, our proposed system introduces secure key distribution between cloud and merchant server named as validate merchant server key distribution protocol (VMSKDP). Our proposed system improves security for mobile payments and reduces attacks like man-in-middle attacks, denial-of-service, etc.
Application of Virtual Reality Technology in Interior Design Under the Background of Big Data

Virtual reality technology is a new design method related to architectural art, widely used in interior space. It can make people experience different shapes or styles through vision and touch and has intuitive and visual characteristics. This paper aims to study the application of virtual reality technology in interior design under the background of big data, accelerate its integration with traditional interior design, and provide new ideas for the design industry's development and innovation. In the experiment, more than 80% of the respondents knew more about the application of VR technology in entertainment film and television but little about interior design. At the same time, 60% of the respondents were satisfied with the experience provided by the companies using VR technology.
Multi-Sensor Data Fusion for Occupancy Detection Using Dempster–Shafer Theory

A data fusion technique has been proposed in this paper for detecting the presence and absence of individuals in a room. It involves the utilization of data from a series of sensors mainly temperature and humidity sensors, for detecting the presence of a person. From the perspective of evidence theory, data collected from every sensor can be viewed as a matter of evidence. A Dempster–Shafer (D-S) theory-based data fusion model is established for modeling and consolidating the pieces of evidence and hence generating an overall speculation of the temperature and humidity level of a room. Testing has been carried out with a dataset that have two classes. At first, the detection is done using various well-known classifiers such as logistic regression which shows an accuracy level of 94%, K-nearest neighbors shows an accuracy of 93%, support vector machines result in an accuracy of 94%, and decision tree classifier and random forest classifier show an accuracy of 92% and 93%, respectively. A subset of the data is used to create class membership probabilities for every attribute for training, and hence, a mass function is created. Finally, D-S rule is applied, and the outcomes suggest that the data fusion method gives a higher accuracy level compared to the only involvement of classifiers.
Robust Makespan Optimization via Genetic Algorithms on the Scientific Workflow Scheduling Problem

Distributed scientific applications are commonly executed as a workflow of data interdependent tasks on a cluster of different machines. Over the last years, the infrastructure used for solving these problems has evolved from clusters of physical machines to virtual resources in a Cloud based on Quality of Service requirements and pay-per-use basis. In these settings, the total execution time of the workflow, i.e., the makespan, is one of the main objectives. The subsequent optimization problem of distributing the tasks on the available resources, called workflow scheduling problem, is often solved by means of metaheuristics. In this paper we propose an improved workflow model that considers disk times in communications costs. To solve the scheduling problem, we devise a genetic algorithm that produces robust schedules. The experimental study showed that the proposed model is able to predict the execution time of the workflow with more precision than the existing ones in a Cloud Infrastructure as a Services system.
Ant Colony Optimization Algorithm for Object Identification in Multi-cameras Video Tracking Systems

The multi-dimensional assignment is one of methods used to associate measurement data with tracks in the analysis of image frames in vision systems. This is a very difficult problem and belongs to the hard NP class. Since accurate methods are very time consuming, there is a need for develop other methods to obtain solutions to this problem in a reasonable time. Among them there is the ant colony optimization heuristic (ACO). ACO has already been proposed for this problem, but only with a static function of desire. The article presents the dynamic function of desire together with data structures and discusses the results of the conducted experiments. The ant algorithm with the dynamic function of desire shows its superiority over the ant algorithm with the static function of desire because it gives a better assignment each time the algorithm is run. The proposed ant algorithm with the new function of desire is not time consuming and can be implemented in multi-cameras vision systems for objects tracking.
Automatic Classification of Fresco Fragments: A Machine and Deep Learning Study

The reconstruction of destroyed frescoes is a complex task: very small fragments, irregular shapes, color alterations and missing pieces are only some of the possible problems that we have to deal with. Surely, an important preliminary step involves the separation of mixed fragments. In fact, in a real scenario, such as a church destroyed by an earthquake, it is likely that pieces of different frescoes, which were close on the same wall, end up mixed together, making their reconstruction more complex. Their separation may be especially difficult if there are many of them and if there are no (or very old) reference images of the original frescoes. A possible way to separate the fragments is to treat this problem as a stylistic classification task, in which we have only parts of an artwork instead of a complete one. In this work, we tested various machine and deep learning solutions on the DAFNE dataset (to date the largest open access collection of artificially fragmented fresco images). The experiments showed promising results, with good performances in both binary and multi-class classification.
Lung Cancer Detection by Classifying CT Scan Images Using Grey Level Co-occurrence Matrix (GLCM) and K-Nearest Neighbours

Lung cancer is the unbridled growth of abnormal cells in the lungs; as the growth of these abnormal cells continues, tumours are formed which obstructs with the natural functioning of the lung. Early cancer diagnoses, combined with treatment and proper medical care, enhances survival and cure rates. This study of lung cancer detection has divided into four stages: a pre-processing stage, image enhancement stage, feature extraction stage, and cancer classification stage. The system thoroughly focuses on detecting lung cancer disease with various image processing and machine learning techniques. The system accepts input in the form of an image called a computerized tomography (CT) scan, which is a medical screening technique used to study and detect lung cancer. This study aims to classify lung cancer into benign and malignant lung cancer using CT scan images. The testing of this system on the given dataset has shown a classification accuracy of 92.37% for determining benign or malignant cancer.
Transient Random Number Seeds in Permissionless Blockchain Systems

Permissionless blockchain systems are highly dependent on probabilistic decision models, for example, the block addition process. If it were possible to use blockchain systems as pseudo-random number generators, they could be used to select, for example, new block proposers. The first step in this process is to embed random number seeds in the blockchain for use in pseudo-random number generation. This paper proposes transient random number seeds (TRNS), which produce random number seeds as part of each transaction. TRNS, belonging to each recipient in a transaction and are confidential, tamper-resistant, unpredictable, collision-resistant, and publicly verifiable. TRNS enable recipients to produce pseudo-random numbers to participate in any process where the blockchain system depends on random selection. The TRNS protocol is highly scalable with constant computational complexity and space complexity linear in the number of transactions per block.
Artificial Intelligence in Digital Media Technology

With the advancement of society and the increase of computer, network and digital media technology, digital media technology has become an indispensable part of the modern service industry. The application of digital media technology in artificial intelligence has enabled the development of all aspects of life, such as the company's internal training, system development, update and maintenance from “quality” to “quantity”. This article mainly introduces the application analysis of artificial intelligence in digital media technology. This paper uses the application analysis of artificial intelligence in digital media technology and proposes a visual media management model. Use artistic innovation methods to analyze and analyze the application of digital media technology in artificial intelligence to improve the standardization and accuracy of digital media. The experimental results of this paper show that the application of visual media and the technology of artistic innovation combined with artificial intelligence technology have increased the innovation rate of digital media by 23%, and improved the interactive synthesis of images and video materials.
QC-PRE: quorum controlled proxy re-encryption scheme for access control enforcement delegation of outsourced data

Proxy re-encryption is used to delegate the task of providing access control to the outsourced data on a cloud storage server. However, the straightforward application of proxy re-encryption requires the cloud storage server to be trusted. The cloud storage servers are however, often, honest-but-curious or untrusted. Towards solving the problem of access control enforcement delegation of outsourced data, we design a quorum controlled proxy re-encryption scheme. We show that, using the proposed scheme, task of enforcing access control is delegated to a set of proxies, such that a quorum of proxies should come together to enforce access control. By distributing trust among multiple proxies, single point of trust is eliminated, and the system is made fault tolerant. We prove the IND-CPA security of the proposed scheme under the DBDHI assumption and show that it satisfies most of the desirable properties of a proxy re-encryption scheme thus outperforming the existing schemes.
Private Circuits with Quasilinear Randomness

A t-private circuit for a function f is a randomized Boolean circuit C that maps a randomized encoding of an input x to an encoding of the output f(x), such that probing t wires anywhere in C reveals nothing about x. Private circuits can be used to protect embedded devices against side-channel attacks. Motivated by the high cost of generating fresh randomness in such devices, several works have studied the question of minimizing the randomness complexity of private circuits. The best known upper bound, due to Coron et al. (Eurocrypt 2020), is $$O(t^2\cdot \log ts)$$ random bits, where s is the circuit size of f. We improve this to $$O(t\cdot \log ts)$$ , including the randomness used by the input encoder, and extend this bound to the stateful variant of private circuits. Our constructions are semi-explicit in the sense that there is an efficient randomized algorithm that generates the private circuit C from a circuit for f with negligible failure probability.
A Strong Geometric Baseline for Cross-View Matching of Multi-person 3D Pose Estimation from Multi-view Images

Reconstructing the 3D pose from multiple people viewed from several cameras is a complex problem. After the 2D human pose estimation is performed in each image separately (e.g. by a deep learning algorithm), the next step is matching the individuals on the different images. In the present contribution, a novel non-deep learning and parameter-free algorithm is developed to this end, which is only based on geometrical cues (i.e. reprojection errors). This is shown to achieve perfect precision and recall figures on a challenging 102-person benchmark.
Challenging Aspects of Data Preserving Algorithms in IoT Enabled Smart Societies

With the technological progress, securing information from attackers is considered a major concerning factor. Society 5.0 is a super-smart society where individuals can tackle many social issues by incorporating various industrial revolutions using smart devices. The influence of these smart devices has made the life of humankind easier and smarter. Due to the parametric changes in size and power requirements, it is tough to apply convention cryptographic procedures on these smart gadgets of Society 5.0 for information security. Generally, standard cryptographic algorithms are too energy-consuming for these embedded devices. One of the significant tools for providing security solutions to these size-limited portable devices is Lightweight Cryptography (LWC). Nowadays, researchers focus on these lightweight security algorithms to optimize maximum throughput and performance with less area and energy consumption. Hence, the current paper presents a detailed analysis of Lightweight Cryptographic cipher approaches on various platforms with their merits and demerits. Also, highlighted the features of each cipher with their performance metrics parameters. Finally, discussed the open research challenges related to the field of Lightweight Cryptography.
A Novel Completeness Test for Leakage Models and Its Application to Side Channel Attacks and Responsibly Engineered Simulators

Today’s side channel attack targets are often complex devices in which instructions are processed in parallel and work on 32-bit data words. Consequently, the state that is involved in producing leakage in these modern devices is not only large, but also hard to predict due to various micro-architectural factors that users might not be aware of. On the other hand, security evaluations—basing on worst case attacks or simulators—explicitly rely on the underlying state: a potentially incomplete state can easily lead to wrong conclusions. We put forward a novel notion for the “completeness” of an assumed state, together with an efficient statistical test that is based on “collapsed models”. Our novel test can be used to recover a state that contains multiple 32-bit variables in a grey box setting. We illustrate how our novel test can help to guide side channel attacks and we reveal new attack vectors for existing implementations. We then demonstrate the application of this test in the context of leakage modelling for leakage simulators and confirm that even the most recent leakage simulators do not capture all available leakage of their respective target devices. Our new test enables finding nominal models that capture all available leakage but do not give a helping hand to adversaries. Thereby we make a first step towards leakage simulators that are responsibly engineered.
Mining Spatio-Temporal Sequential Patterns Using MapReduce Approach

Spatio-temporal sequential pattern mining (STSPM) plays an important role in many applications such as mobile health, criminology, social media, solar events, transportation, etc. Most of the current studies assume the data is located in a centralized database on which a single machine performs mining. Thus, the existing centralized algorithms are not suitable for the big data environment, where data cannot be handled by a single machine. In this paper, our main aim is to find out the Spatio-temporal sequential patterns from the event data set using a distributed framework suitable for mining big data. We proposed two distributed algorithms, namely, MR-STBFM (MapReduce based spatio-temporal breadth first miner), and MR-SPTreeSTBFM (MapReduce based sequential pattern tree spatio-temporal breadth first miner). These are the distributed algorithms for mining spatio-temporal sequential patterns using Hadoop MapReduce framework. A spatio-temporal tree structure is used in MR-SPTreeSTBFM for reducing the candidate generation cost. This is an extension to the proposed MR-STBFM algorithm. The tree structure significantly improves the performance of the proposed approach. Also, the top-most significant pattern approach has been proposed to mine the top-most significant sequential patterns. Experiments are conducted to evaluate the performance of the proposed algorithms on the Boston crime dataset.
Sampling with Varying Probabilities

We have been covering so long the design-based contents in survey sampling. We have talked about general sampling designs p, expectation, bias, mean square error, variance, standard error, confidence interval, inclusion probabilities, existence theorems, concepts of sufficiency, minimal sufficiency, complete class theorems, etc. with reference to general designs of course. But we have had detailed discussions related only to equal probability sampling or Simple Random Sampling only, though separately for with and without replacement cases. But now we intend to refer to several specific cases of sampling with unequal or varying probabilities. Of course, we have to cite reasons for advocating their merits and privileges for employing them in practice.