Reviewerly/exHarmony huggingface dataset:
A Method for Curation of Web-Scraped Face Image Datasets

Web-scraped, in-the-wild datasets have become the norm in face recognition research. The numbers of subjects and images acquired in web-scraped datasets are usually very large, with number of images on the millions scale. A variety of issues occur when collecting a dataset in-the-wild, including images with the wrong identity label, duplicate images, duplicate subjects and variation in quality. With the number of images being in the millions, a manual cleaning procedure is not feasible. But fully automated methods used to date result in a less-than-ideal level of clean dataset. We propose a semi-automated method, where the goal is to have a clean dataset for testing face recognition methods, with similar quality across men and women, to support comparison of accuracy across gender. Our approach removes near-duplicate images, merges duplicate subjects, corrects mislabeled images, and removes images outside a defined range of pose and quality. We conduct the curation on the Asian Face Dataset (AFD) and VGGFace2 test dataset. The experiments show that a state-of-the-art method achieves a much higher accuracy on the datasets after they are curated. Finally, we release our cleaned versions of both datasets to the research community.
A Traffic Density Estimation Model Based on Crowdsourcing Privacy Protection

Acquiring traffic condition information is of great significance in transportation guidance, urban planning, and route recommendation. To date, traffic density data are generally acquired by road sound analysis, video data analysis, or in-vehicle network communication, which are usually financially or temporally expensive. Another way to get traffic conditions is to collect track data by crowdsourcing. However, this way lead to a greater risk of leaking users’ privacy. To avoid the risk, this article proposes a traffic density estimation model based on crowdsourcing privacy protection. First, in the acquisition process of the track data by crowdsourcing, dual servers are employed for transmission, and homomorphic encryption is carried out to encrypt the data to protect the data from being leaked during transmission. Second, sampling is implemented for randomization and anonymization to reduce the spatial continuity and temporal continuity of position data. In this way, the intermediate server cannot acquire users’ original data, and the main server cannot obtain users’ personal information. Finally, before data transmission, Laplace noising is performed on the users’ local position data to further protect the original location information. The proposed algorithm in this study realizes that only users have their original track data, and the servers involved in the work cannot infer the original track data, which ensures the real security of user privacy. The proposed algorithm was verified with the track data from the Didi Gaia Data Opening Plan. The experimental results showed that the proposed algorithm could still maintain the validity of data analysis results and the security of user data privacy after homomorphic encryption, noise addition, and sample collection, and displayed good robustness and scalability.
Learning to Select Elements for Graphic Design

Selecting elements for graphic design is essential for ensuring a correct understanding of clients' requirements as well as improving the efficiency of designers before a fine-designed process. Some semi-automatic design tools proposed layout templates where designers always select elements according to the rectangular boxes that specify how elements are placed. In practice, layout and element selection are complementary. Compared to the layout which can be readily obtained from pre-designed templates, it is generally time-consuming to mindfully pick out suitable elements, which calls for an automation of elements selection. To address this, we formulate element selection as a sequential decision-making process and develop a deep element selection network (DESN). Given a layout file with annotated elements, new graphical elements are selected to form graphic designs based on aesthetics and consistency criteria. To train our DESN, we propose an end-to-end, reinforcement learning based framework, where we design a novel reward function that jointly accounts for visual aesthetics and consistency. Based on this, visually readable and aesthetic drafts can be efficiently generated. We further contribute a layout-poster dataset with exhaustively labeled attributes of poster key elements. Qualitative and quantitative results indicate the efficacy of our approach.
Asterisk

Labeling datasets is one of the most expensive bottlenecks in data preprocessing tasks in machine learning. Therefore, organizations, in many domains, are applying weak supervision to produce noisy labels. However, since weak supervision relies on cheaper sources, the quality of the generated labels is problematic. Therefore, in this article, we present Asterisk , an end-to-end framework to generate high-quality, large-scale labeled datasets. The system, first, automatically generates heuristics to assign initial labels. Then, the framework applies a novel data-driven active learning process to enhance the labeling quality. We present an algorithm that learns the selection policy by accommodating the modeled accuracies of the heuristics, along with the outcome of the generative model. Finally, the system employs the output of the active learning process to enhance the quality of the labels. To evaluate the proposed system, we report its performance against four state-of-the-art techniques. In collaboration with our industrial partner, IBM, we test the framework within a wide range of real-world applications. The experiments include 10 datasets of varying sizes with a maximum size of 11 million records. The results illustrate the effectiveness of the framework in producing high-quality labels and achieving high classification accuracy with minimal annotation efforts.
Cybersecurity Drill Test Using Phishing Attack: A Pilot Study of a Large Financial Services Firm in Thailand

This paper presents a pilot study about cybersecurity drill tests within a large financial services company in Thailand using a phishing technique. After conducting simulation by sending phishing emails to more than 21,000 users nationwide, including executives and employees, it has been found that 73% of executives and 77% of employees ignored the phishing emails, whereas, 12% of executives and 15% of employees opened, clicked and filled-in the password. The numbers are higher than the result from earlier research provided by Verizon. Therefore, the result from this can be used to increase awareness and improve the cybersecurity within this organization by proposing the training program to their staff, as well as conducting the other cybersecurity drill tests in future work. Besides, this study can be an example for other companies to improve organizational cybersecurity.
Dual-factor Generation Model for Conversation

The conversation task is usually formulated as a conditional generation problem, i.e., to generate a natural and meaningful response given the input utterance. Generally speaking, this formulation is apparently based on an oversimplified assumption that the response is solely dependent on the input utterance. It ignores the subjective factor of the responder, e.g., his/her emotion or knowledge state, which is a major factor that affects the response in practice. Without explicitly differentiating such subjective factor behind the response, existing generation models can only learn the general shape of conversations, leading to the blandness problem of the response. Moreover, there is no intervention mechanism within the existing generation process, since the response is fully decided by the input utterance. In this work, we propose to view the conversation task as a dual-factor generation problem, including an objective factor denoting the input utterance and a subjective factor denoting the responder state. We extend the existing neural sequence-to-sequence (Seq2Seq) model to accommodate the responder state modeling. We introduce two types of responder state, i.e., discrete and continuous state, to model emotion state and topic preference state, respectively. We show that with our dual-factor generation model, we can not only better fit the conversation data, but also actively control the generation of the response with respect to sentiment or topic specificity.
TVseer: A visual analytics system for television ratings

The television ratings provide an effective way to analyze the popularity of TV programs and audiences' watching habits. Most previous studies have analyzed the ratings from a single perspective. Few efforts have integrated analysis from different perspectives and explored the reasons for changes in ratings. In this paper, we design a visual analysis system called TVseer to analyze audience ratings from three perspectives: TV channels, TV programs, and audiences. The system can help users explore the factors that affect ratings, and assist them in decisions about program productions and schedules. There are six linked views in TVseer: the channel ratings view and program ratings view show ratings change information from the perspective of TV channels and programs respectively; the overlapping program competition view and the same-type program competition view indicate the competitive relationships among programs; the audience transfer view shows how audiences are moving among different channels; the audience group view displays audience groups based on their watching behavior. Besides, we also construct case studies and expert interviews to prove our system is useful and effective.
Body Image: Neural Basis of ‘Negative’ Phantom Limbs

<h2>Summary</h2> Patients with the neurological syndrome known as body integrity dysphoria express a desire to amputate seemingly healthy parts of their body. A new study has found that this condition is linked to altered functional connectivity in a distributed neural network underlying body image.
Evaluation of Shared Resource Allocation Using SAND for ABR Streaming

Adaptive bitrate media streaming clients adjust the quality of media content depending on the current network conditions. The shared resource allocation (SRA) feature defined in MPEG-SAND (server and network assisted DASH) allows servers to allocate bandwidth to streaming clients. This enables coordination and prioritization of clients that are connected to the same network bottleneck (e.g., to maximize the number of clients that can play back a stream fluently). In this article, we evaluate different bandwidth limitation strategies and analyze the effects on the clients. For this purpose, a testbed using multiple Raspberry Pis was created. The results show that in various scenarios, SRA improves the fairness and the QoE of streaming sessions. Solely allocating a maximum quality level to the client is not sufficient in some cases. Therefore, additional means, such as limiting bandwidth on the client or traffic shaping with software-defined networking for SRA, are evaluated.
Complex Hadamard matrices, instantaneous uniform mixing and cubes

We study the continuous-time quantum walks on graphs in the adjacency algebra of the n-cube and its related distance regular graphs.
Evaluation of Content Compaction in Assamese Language

Text summarization is the task of condensing the input text documents into a shorter version by retaining the overall meaning and information of the original document. Though plenty of text compaction research works in English and other languages have been done till date, but text compaction in Assamese language is still lagging behind due to the limitations of resources for text compaction. This paper proposes a content compaction approach for Assamese Text to generate a summary by incorporating statistical and linguistic features and makes an attempt to extract the relevant points in the summary from the input Assamese document. The statistical features applied in this approach incorporate a new modified tf-idf, sentence positioning, length of sentences and identification of numericals. Whereas the linguistic features employed include identifying the nouns, cue words and cue phrases. To contrast the summaries that are generated both automatically and by humans, the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) framework is used. Furthermore, our proposed approach is also compared with text summarization approaches by using traditional tf-idf. It is observed that this approach has yielded impressive results while summarizing Assamese documents with average F-measure of 0.633 at the compression ratio of 50%.
A Psychophysics-inspired Model of Gaze Selection Performance

Eye gaze promises to be a fast and intuitive way of interacting with technology. Importantly, the performance of a gaze selection paradigm depends on the eye tracker used: Higher tracking accuracy allows for selection of smaller targets, and higher precision and sampling rate allow for faster and more robust interaction. Here we present a novel approach to predict the minimal eye tracker specifications required for gaze-based selection. We quantified selection performance for targets of different sizes while recording high-fidelity gaze data. Selection performance across target sizes was well modeled by a sigmoid similar to a psychometric function. We then simulated lower tracker fidelity by adding noise, a constant spatial bias, or temporal sub-sampling of the recorded data while re-fitting the model each time. Our approach can inform design by predicting performance for a given interface element and tracker fidelity or the minimal element size for a specific performance level.
Decentralized Cross-organizational Application Deployment Automation: An Approach for Generating Deployment Choreographies Based on Declarative Deployment Models

Various technologies have been developed to automate the deployment of applications. Although most of them are not limited to a specific infrastructure and able to manage multi-cloud applications, they all require a central orchestrator that processes the deployment model and executes all necessary tasks to deploy and orchestrate the application components on the respective infrastructure. However, there are applications in which several organizations, such as different departments or even different companies, participate. Due to security concerns, organizations typically do not expose their internal APIs to the outside or leave control over application deployments to others. As a result, centralized deployment technologies are not suitable to deploy cross-organizational applications. In this paper, we present a concept for the decentralized cross-organizational application deployment automation. We introduce a global declarative deployment model that describes a composite cross-organizational application, which is split to local parts for each participant. Based on the split declarative deployment models, workflows are generated which form the deployment choreography and coordinate the local deployment and cross-organizational data exchange. To validate the practical feasibility, we prototypical implemented a standard-based end-to-end toolchain for the proposed method using TOSCA and BPEL.
Real-Time Deep-Learning Based Traffic Volume Count for High-Traffic Urban Arterial Roads

Traffic volume survey is important for the relevant authorities in estimating road usage and traffic trends for short and long-term traffic facilities planning and design. Commonly, the survey is done manually where the human observers have to be at the actual site throughout the survey period. Not only the method may cause danger to the observers, but it also resources intensive as the traffic volume is increasing, such as in urban arterials. Thus, in this paper, a deep-learning-based traffic volume count system is proposed and extensively tested with 48 high-traffic video clips captured from cameras temporarily installed at four selected urban arterial roads (estimated AADT more than 50,000 and 100,000). For testing, the video clips are split into 5-minutes and 15-minutes duration. Then the accuracy of each clip is evaluated based on the error between system output and the manual ground-truth. The average accuracy for the four camera views is 97.68%, 93.84%, 97.7%, and 94.23% respectively. The system is also able to run in real-time with the average processing time of 37.27ms per frame. Thus, the proposed system is suitable to be used in the traffic volume survey.
Kinect Based Virtual Referee For Table Tennis Game: TTV (Table Tennis Var System)

Today's game quality is more affected by referee decisions. The history shows that the error in a referee's decision may change the whole result of a game. It seems to rely on just the referee's decision and always combines with the errors. This fact is even bolded in important games like the World Cup or Olympics which may cause the rank of the team, players, etc. Recently, to solve this problem, the new concept of VAR (video assistant referee) has been introduced and had also been hired during the football World Cup match 2018. Even some methods like video check-ins in volleyball games or bypass signal in Swordsmanship (sword fighting) long back was used. But still, so many demand games are missed. Statistically, almost approximately 8 hundred million people are playing table tennis in the world but due to the ball speed in this game which sometimes increases up to 69.9 miles per hour (112.4931 km/h) which brings the limitation to hire and use the VAR systems. In this research paper, a novel VAR system (TTV system) based on Kinect and Raspberry pi has been presented. The main aspect of this design is to have a portable system which can be used for any type of LCD system and help the referees to count the ball better during the game to have a more qualified game. In the proposed TTV algorithm, the two methods of contour and depth are merged to have better ball detection. The experiment results in showing the proposed algorithm and systems success in real game and its ability to implement in the real game which has not been reported before.
Halting Time is Predictable for Large Models: A Universality Property and Average-Case Analysis

Average-case analysis computes the complexity of an algorithm averaged over all possible inputs. Compared to worst-case analysis, it is more representative of the typical behavior of an algorithm, but remains largely unexplored in optimization. One difficulty is that the analysis can depend on the probability distribution of the inputs to the model. However, we show that this is not the case for a class of large-scale problems trained with first-order methods including random least squares and one-hidden layer neural networks with random weights. In fact, the halting time exhibits a universality property: it is independent of the probability distribution. With this barrier for average-case analysis removed, we provide the first explicit average-case convergence rates showing a tighter complexity not captured by traditional worst-case analysis. Finally, numerical simulations suggest this universality property holds for a more general class of algorithms and problems.
A Performance Study of Bio-Inspired Algorithms in Autonomous Landing of Unmanned Aerial Vehicle

The complete operational automation of fixed wing Unmanned Aerial Vehicle (UAV) involves the autonomous operations across take-off, cruising and landing. Among all these stages the landing stage is the most crucial one. During landing, it is important for the UAV to maintain a constant speed and glide slope to ensure the stability and a successful touchdown on the runway. Also, it is important for a UAV to estimate the accurate point of landing in a minimal amount of time. Embedding Bio-inspiring algorithms in UAV control systems helps in accurate estimation of the landing point in a minimal amount of time. In this research work, the Bio-inspired optimization algorithms Bats Optimization Algorithm, Moth Flame Optimization Algorithm and Artificial Bee Colony Algorithm are used in determining the coordinates (points) of the computed path and to determine the optimal point of landing which ensures the above said parameters are within the operational limits of the UAV. The objective of this research work is to determine the path from the computed points and to find the optimal landing point in a minimal amount of time. The difference between the original points of the actual path and the derived computed points of the estimated path is measured as the error rate. The performance of the algorithms is analyzed in terms of two trade-off parameters, the time taken to compute the landing point and the accuracy in predicting the landing point. The empirical results show that the Moth Flame Optimization Algorithm takes less time to compute the optimal point with minimal error among the three optimization algorithms taken up for the study.
A heuristics for HTTP traffic identification in measuring user dissimilarity

Abstract The prevalence of HTTP web traffic on the Internet has long transcended the layer 7 classification, to layers such as layer 5 of the OSI model stack. This coupled with the integration-diversity of other layers and application layer protocols has made identification of user-initiated HTTP web traffic complex, thus increasing user anonymity on the Internet. This study reveals that, with the current complex nature of Internet and HTTP traffic, browser complexity, dynamic web programming structure, the surge in network delay, and unstable user behavior in network interaction, user-initiated requests can be accurately determined. The study utilizes HTTP request method of GET filtering, to develop a heuristic algorithm to identify user-initiated requests. The algorithm was experimentally tested on a group of users, to ascertain the certainty of identifying user-initiated requests. The result demonstrates that user-initiated HTTP requests can be reliably identified with a recall rate at 0.94 and F -measure at 0.969. Additionally, this study extends the paradigm of user identification based on the intrinsic characteristics of users, exhibited in network traffic. The application of these research findings finds relevance in user identification for insider investigation, e-commerce, and e-learning system as well as in network planning and management. Further, the findings from the study are relevant in web usage mining, where user-initiated action comprises the fundamental unit of measurement.
An Improved Reversible Data Hiding Through Encryption Scheme with Block Prechecking

Reversible data hiding through encryption is a recently introduced technique in the area of information security. In a reversible data hiding through encryption scheme, both the image encryption process and data hiding task are combined into a single process. In this paper, we improve the existing reversible data hiding through encryption scheme by introducing a block prechecking mechanism to reduce the bit error rate. The key idea of the proposed scheme is that during image encryption, the original image will be divided into non-overlapping blocks of size n×n pixels and in each block, we can embed three-bit from the secret message. One specific pseudo-random byte stream will be used to encrypt a given image block based on the three-bit data that we want to embed into it. The experimental study of the proposed scheme on the standard medical images downloaded from OsriX data set shows that the proposed scheme outperforms the existing schemes in terms of bit error rate without compromising the embedding rate.