Reviewerly/exHarmony huggingface dataset:
Real-time Spatio-Temporal Action Localization in 360 Videos

Spatio-temporal action localization of human actions in a video has been a popular topic over the past few years. It tries to localize the bounding boxes, the time span and the class of one action, which summarizes information in the video and helps humans understand it. Though many approaches have been proposed to solve this problem, these efforts have only focused on perspective videos. Unfortunately, perspective videos only cover a small field-of-view (FOV), which limits the capability of action localization. In this paper, we develop a comprehensive approach to real-time spatio-temporal localization that can be used to detect actions in 360 videos. We create two datasets named UCF-101-24-360 and JHMDB-21-360 for our evaluation. Our experiments show that our method consistently outperforms other competing approaches and achieves a real-time processing speed of 15fps for 360 videos.
A novel fusion approach in the extraction of kernel descriptor with improved effectiveness and efficiency

Image representation using feature descriptors is crucial. A number of histogram-based descriptors are widely used for this purpose. However, histogram-based descriptors have certain limitations and kernel descriptors (KDES) are proven to overcome them. Moreover, the combination of more than one KDES performs better than an individual KDES. Conventionally, KDES fusion is performed by concatenating them after the gradient, colour and shape descriptors have been extracted. This approach has limitations in regard to the efficiency as well as the effectiveness. In this paper, we propose a novel approach to fuse different image features before the descriptor extraction, resulting in a compact descriptor which is efficient and effective. In addition, we have investigated the effect on the proposed descriptor when texture-based features are fused along with the conventionally used features. Our proposed descriptor is examined on two publicly available image databases and shown to provide outstanding performances.
Digital Voice Assistants: A new kind of user agent

Digital voice assistants provide a new way of browsing and interacting online. Now we can simply speak and listen to our devices, almost as if to a human assistant. However, the convenience of using our voice comes at a cost: On a cue, audio of our speech is streamed to the cloud of the service providers, where they are retained and processed at their discretion. These speech data contain more information about us than simple text, including features of our voice, which can be used to identify us, and of our mood and energy level, which can be used to profile us to an extent that may not be justified by the convenience we get in return. To mediate between the interests of the user and those of the service providers, the General Data Protection Regulation (GDPR) puts the user in control of their personal data. In this paper, we show that speech data qualify as personal data according to the GDPR; however, in practice service providers can operate around the regulation, and so, it appears ineffective. This is risky given how forcefully voice assistants are pushed to market by companies who, by many accounts, operate under surveillance capitalistic objectives to control user behavior.
A new approach of staff lines removal for manuscript documents

Staff lines detection process facilitates handwriting recognition and segmentation, especially for music symbols. It took part in various research works that allow manuscripts' recognition. In another way, staff lines removal remains a challenge for research by the fact that most document symbols are superimposed on staff lines and their removal causes the alteration of important notes and prevents the recognition process. In this paper, we present a new approach of staff lines removal, keeping intact the manuscript symbols and preserve the overall information shape. The proposed technique has been supported by various experiments where results show its efficiency and endorsed its competition against similar works.
Machine Learning for Strategic Decision Making during COVID-19 at Higher Education Institutes

Machine learning is becoming driving force for strategic decision making in higher educational institutions and it calls for cooperation between stakeholders and the use of efficient computation methods.Contrariwise, making decisions might consume much time, if there is no use of data and computational methods during the process of decision making.The utilization of machine learning is essential when coming up with an ultimate analysis of data and decision making.Besides, the technology which is under artificial intelligence could facilitates incredible output for educational institutes when it came to decision making.This paper analyses the output generated using machine learning algorithms that help in prediction of no detriment policy applicability rate in the case of e-learning during COVID-19.The study investigates the performance of machine learning algorithms for strategic decision making in the higher educational institutes, Global College of Engineering and Technology in particular, whether no detriment policy will be applicable for a particular student based on students performance before COVID-19.The study shown that Random Forest machine learning algorithm performance is higher as compare to Support Vector Machine, Decision Tree and Navie Bayes.
Exploring Lawful Hacking as a Possible Answer to the "Going Dark" Debate

The debate on government access to encrypted data, popularly known as the “going dark” debate, has intensified over the years. On the one hand, law enforcement authorities have been pushing for mandatory exceptional access mechanisms on encryption systems in order to enable criminal investigations of both data in transit and at rest. On the other hand, both technical and industry experts argue that this solution compromises the security of encrypted systems and, thus, the privacy of their users. Some claim that other means of investigation could provide the information authorities seek without weakening encryption, with lawful hacking being one of the most suggested alternatives. “Lawful hacking,” also known as “government hacking,” consists in the deployment, by investigative authorities, of tools that allow for the intrusion into computer systems, enabling access to its contents. Although this form of investigation seems to be essential in an increasingly connected society, it is important to understand security and privacy risks of different lawful hacking regulatory approaches. Considering that some countries are already enacting legal frameworks related to it, I aim to highlight the issues that should be properly addressed in order to position lawful hacking as one of the viable answers to the “going dark” debate.
Is Bitcoin a Decentralized Payment Mechanism?

We make a distinction between centralized, decentralized, and distributed payment mechanisms. A centralized payment mechanism processes a transaction using a trusted third party. A decentralized payment mechanism processes a transaction between the parties to the transaction. A distributed payment mechanism relies on the network of users to process a transaction on a shared ledger. We maintain that bitcoin is neither a centralized nor decentralized payment mechanism. It is, instead, a distributed payment mechanism. We then consider decentralized and centralized aspects of the broader bitcoin payment space.
A Study on Classification Techniques based on Opinions

Abstract Sentiment analysis or opinion mining is one of the considerable undertakings of Natural Language Processing. Sentiment analysis is a quick-growing investigation area in computer science. It includes a way of improvement for the collection and assessment of comments or opinions about legislation, laws, and opinions of products, and so forth, which are posted on the internet. Opinion mining analyzes the opinions given by people. In sentiment analysis, the customer feedback, review, shopping site review, online purchase reviews, politics opinions are considered for the analysis. Sentiment analysis is about detecting the emotions i.e. sad, happy, angry, excited, etc. The main objective of opinion mining is characterizing what the public think and comment. This review paper features certain discussions concerning the opinion-based classification and used approaches for the extraction of feeling assessment and opinion mining. Paper presents a comparison between different classification techniques. Applications and challenges of opinion mining are also discussed in this paper.
Extraction of Frame Sequences in the Manga Context

Manga are one of the most popular forms of comics consumed on a global level. Unfortunately, this kind of media was not designed for digital consumption, and consequently its format does not fit well into small areas, such as smartphone screens. In order to cope with this issue, in this paper we propose a novel approach to comics segmentation and sequencing by taking advantage of existing machine learning concepts which are used to generate an artificial intelligence (AI) capable of correctly detecting panels within an image. The regions proposed by the AI are then used to generate a grid that acts as anchor points for a mobile application guiding the reader during navigation and enabling full Manga responsiveness. The developed approach achieves overall better performances in terms of precision and recall, as well as higher fault tolerance than state-of-the-art approaches. The reliability of this method is also considered largely satisfactory for real-world scenarios, so that we are about to finalize an app implementing the method to be spread soon; additionally, future work will be devoted to generalize our approach to all the comics formats.
Data Synthesis for Document Layout Analysis

Layout analysis plays an important role in various document image processing tasks such as OCR and document understanding, and the methods based on deep learning have achieved significant achievements. In recent years, pre-training and transfer learning techniques have become a common practice in a variety of computer vision and natural language processing tasks. In this paper, we present an efficient approach of data synthesis for pretraining deep learning models in document layout analysis. The synthesized data is automatically annotated based on heuristic rules, and then applied to the PubLayNet pre-trained models. The models are fine-tuned with real document layout data. Three types of document elements are taken into account: text lines, tables, and figures/images. The experiments demonstrate that the pre-training model with synthesized data is very effective for transfer learning on different document domains.
Gaze Based Interaction for Object Classification in Reconnaissance Missions Using Highly Automated Platforms

A gaze based interface was used as the basis for a interaction system supporting a remote operator of a ground reconnaissance robot when classifying objects previously identified by a software. Using the gaze point as a cursor, a pre-selection of the object in the sightline is made, allowing the user to effect the symbol via a haptic interface. An extension of the system enables the user to manually re-adjust the pre-selection if highlights are overlapping. The results from a first exploration were encouraging; the system was easily understood and used but does require further testing with scenarios that are more complex.
Lightweight Real-time Fall Detection using Bidirectional Recurrent Neural Network

As the world's population is aging, the home care systems for elderly people have been getting high attention. According to the National Council on Aging, every 11 seconds, an older adult is treated in the emergency room for a fall, and every 19 minutes, an older adult dies from a fall. The number of single households is also increasing with an aging society. In a single household, there is no one to help the elderly when they fall. This could lead to serious problems such as disability or death. In this paper, we propose a lightweight real-time system for fall detection, distinguished from other activities of daily living (ADL). The entire system is divided into a preprocessing and prediction part. With the system, falls and ADLs can be distinguished with more than 92% accuracy which is higher than the existing approach even without any additional resampling method.
Implementation of rabin-karp algorithm to determine the similarity of synoptic gospels

The Synoptic Gospels have been a mystery for several hundred years in the study of the New Testament. One problem that stands out is the similarity of the content inside. Not only the order of the story but also the order of the words. To analyze this issue, this research used Rabin-Karp Algorithm to determine the similarities of the first three Gospels in the New Testament. The algorithm is perfect for searching multiple patterns in two documents. There are several stages in this calculation process: first, verses in each same categories will be splitted by x letters called substrings. Second, each letter in the substring will have a hashing process and produce a series of numbers. Third, a set of number combinations will look for unique numbers called fingerprints. Finally, comparing the fingerprints on document A and B before calculating the similarity using dice similarity coefficent. The results are both Matthew and Luke have a close relationship with Mark. As a conclusion, Matthew is closely related to Mark (59.13%) based on their contents compared to Luke (52.85%) or Mark against Luke (54.41%).
DCR: Disentangled component representation for sketch generation

We present a simple end-to-end model based on deep learning to automatically decompose sketched objects into components by disentangling the visual representation. The performance of visual representation learning based models degrades as categories increase. Rather than building a mapping from a static image to the whole sketch sequences, we propose an interpretable disentangled representation of sketch to understand component concepts and the relationship among such concepts. Our model takes the binary image of a sketched object and produces a component stroke sequence set corresponding to key components in the sketch. Experiments show that our method significantly outperforms all baselines quantitatively at the degree of disentanglement, and our method is more stable while training on tens of categories.
Performance Analysis of Sales Big Data Processing using Hadoop and Hive in Cloud Environment

Nowadays, big data gains much attention from academics and IT industries. This is due to the extraordinary current growth of data that must be accompanied by a variety of qualified data storage and processing techniques to overcome the5 V's challenge of big data. This research is aimed to conduct a performance analysis of big data processing. The sales data will be processed in a parallel scheme on the cloud server and then managed using Hadoop and hive. The research shows that the more VMs used, the lower processing time needed, but this is inversely proportional to the CPU time required. Whereas, from the side of block size testing the research result shows that the decrease in the time of query execution is very visible by the change in the use of block size from 2MB to 4MB and 8MB, but the change in the blocksize size from 4MB to 8MB does not significantly affect the speed of query execution.
Analysis on the Maintainability of Smart Meter Software for Full Lifetime

Abstract With the deepening of Internet concept and electricity marketization reform, more requirements are put forward for metering equipment, and the scale and complexity of software also increase. The new generation of intelligent electricity meters meet the IR46 standard and adopt the multi-core modular design scheme, which also puts forward higher requirements for the maintainability of software. This paper starts from the key control points of the whole life cycle of intelligent electricity meters and combines the characteristics of software testing, so as to analyze and evaluate the maintainability of the software. Firstly, the factors that influence the maintainability of watt-hour meter products are analyzed, and the software key data is considered from the perspective of software development and design. Then, the index system of the maintainability of the intelligent software is established, and the key points of the maintainability test and the weight of each index are analyzed in combination with the testing technology of the intelligent meter software and the analytic hierarchy process.
SPOD: Syntactic Profiler of Dutch

SPOD is a tool for Dutch syntax in which a given corpus is analysed according to a large number of predefined syntactic characteristics. SPOD is an extension of the PaQu (”Parse and Query”) tool (Odijk et al. 2017). SPOD is available for a number of standard Dutch corpora and treebanks. In addition, you can upload your own texts which will then be syntactically analysed. SPOD will run a potentially large number of syntactic queries in order to show a variety of corpus properties, such as the number of main and subordinate clauses, types of main and subordinate clauses, and their frequencies, average length of clauses (per clause type: e.g. relative clauses, indirect questions, finite complement clauses, infinitival clauses, finite adverbial clauses, etc.). Other syntactic constructions include comparatives, correlatives, various types of verb clusters, separable verb prefixes, depth of embedding etc. SPOD allows linguists to obtain a quick overview of the syntactic properties of texts, for instance with the goal to find interesting differences between text types, or between authors with different backgrounds or different age. In the paper, we describe the SPOD tool in some more detail, and we provide a case study, illustrating the type of investigations which are enabled andfacilitated by SPOD. Most of the syntactic properties are implemented in SPOD by means of relatively complicated XPath 2.0 queries, and as such SPOD also provides examples of relevant syntactic queries, which may otherwise be relatively hard to define for non-technical linguists. SPOD is available via https://www.let.rug.nl/alfa/paqu/spod
A GA-based Data Sanitization for Hiding Sensitive Information with Multi-Thresholds Constraint

In this work, we propose a new concept of multiple support thresholds to sanitize the database for specific sensitive itemsets. The proposed method assigns a stricter threshold to the sensitive itemset for data sanitization. Furthermore, a genetic-algorithm (GA)-based model is involved in the designed algorithm to minimize side effects. In our experimental results, the GA-based PPDM approach is compared with traditional compact GA-based model and results clearly showed that our proposed method can obtain better performance with less computational cost.
Comparing Sequential and Parallel Word Counting Methods in Finding Commonly Used Words by Different Authors

Parallel computing is exposing to everyday life and becomes a target for parallelism experiments. This paper is a compression between sequential and parallel methods in word counting to find the commonly used words by different authors in a selected public dataset that includes around 500,000 quotes. Python multiprocessing library with a pool of worker processes was used for this purpose. The sequential process (Sequential Method-SM) is grouped into sub-processes "operations" to evaluate the execution time in each operation, and then the operation that consumes the largest portion of execution time is parallelized with multiprocess pool function (Partially Parallelized Method-PPM). After that, the whole process is parallelized to evaluate the overall performance (Fully Parallelized Method-FPM). Results show improved efficiency in the overall parallelized process by 48%, and the most used words by selected authors appear relevant to the authors' profession.
A Survey On Cloud-Based Distributed Computing System Frameworks

The use of data is increasing steadily in the modern era of technology. That produced the term big data. Many companies are interested in analyzing this data, which amounts to several terabytes. The term distributed computing system appears as an effective technique for analyzing big data. Businesses have sought to use cloud resources to implement distributed computing in order to reduce costs and increase productivity. Distributed computing system faces the hurdle of significant material cost for providing related equipment. To avoid the financial cost problem, researchers have developed frameworks and tools for implementing distributed computing operations by using the redundant resources of cloud computing. This paper presents the advantages and disadvantages of a distributed computing system as well as analyzes traditional systems of distributed computing and concludes with a survey of frameworks for using cloud computing resources to implement distributed computing system operations at a lower cost.
A Business Intelligence model to analyze consumer opinions on social networks using machine learning techniques

In recent years, social networks have become a rich platform of information and data shared by users in almost all sectors of society. Therefore, the widespread use of the social network generates a huge amount of unstructured social data that must be analyzed and well structured. It is for this purpose that the sentiment analysis has received considerable attention. Sentiment analysis is presented as a text organization used to classify the feelings and opinions expressed by the user in different ways (negative, positive, neutral, favorable, unfavorable, etc.).Due to the massive amount of data available on social networks, sharing opinions, reviews, comments, and suggestions, it is necessary to extract, analyze, and consolidate for improved decision-making. Sentiment analysis presents an effective and efficient opinion of consumers in real-time, which can greatly affect business decision-making. To solve this problem sentiment analysis and machine learning techniques have been united because machine learning models can be beneficial in the field of business intelligence.This article proposes a BI model to observe in real-time the satisfaction and the reviews of consumers on a product or service on social networks, notably Facebook and Twitter, based on machine learning techniques.
Learning deep discriminative embeddings via joint rescaled features and log-probability centers

Recently softmax based loss functions have surged to advance image classification and face verification. Most efforts boost discrimination of the softmax loss by using novel angular margins in varying ways, but few analyze where the discrimination truly comes from whilst considering the power of relieving the overfitting to enhance the softmax loss. In this paper, we firstly delve into such mainstream of softmax based loss functions in theory, and recognize the importance of easing overfitting to the softmax loss. In terms of such analysis, this paper intends to bring the softmax loss up to the competitive level with current well-behaved loss functions. We do this in two ways: (1) regularizing the softmax to relieve the overfitting by learning the log-probability centers, and (2) rescaling deep embeddings of the softmax with a constant scale to further enhance inter-class separability in Euclidean space. We call the resulting loss function rLogCenter loss for short. Simple and interpretable as our loss is, it guides CNNs to induce performance gains in the experiments of both image classification and face verification.