Reviewerly/exHarmony huggingface dataset:
A Novel Method for Estimating Monocular Depth Using Cycle GAN and Segmentation

Modern image processing techniques use three-dimensional (3D) images, which contain spatial information such as depth and scale, in addition to visual information. These images are indispensable in virtual reality, augmented reality (AR), and autonomous driving applications. We propose a novel method to estimate monocular depth using a cycle generative adversarial network&nbsp;(GAN) and segmentation. In this paper, we propose a method for estimating depth information by combining segmentation. It uses three processes: segmentation and depth estimation, adversarial loss calculations, and cycle consistency loss calculations. The cycle consistency loss calculation process evaluates the similarity of two images when they are restored to their original forms after being estimated separately from two adversarial losses. To evaluate the objective reliability of the proposed method, we compared our proposed method with other monocular depth estimation (MDE) methods using the NYU Depth Dataset V2. Our results show that the benchmark value for our proposed method is better than other methods. Therefore, we demonstrated that our proposed method is more efficient in determining depth estimation.
Forward and Backward Private DSSE for Range Queries

Due to its capabilities of searches and updates over the encrypted database, the dynamic searchable symmetric encryption (DSSE) has received considerable attention recently. To resist leakage abuse attacks, a secure DSSE scheme usually requires forward and backward privacy. However, the existing forward and backward private DSSE schemes either only support single keyword queries or require more interactions between the client and the server. In this article, we first give a new leakage function for range queries, which is more complicated than the one for single keyword queries. Furthermore, we propose a concrete forward and backward private DSSE scheme by using a refined binary tree data structure. Finally, the detailed security analysis and extensive experiments demonstrate that our proposal is secure and efficient, respectively.
A theory of dual channel constraints

The surprising predictability of source code has triggered a boom in tools using language models for code. Code is much more predictable than natural language, but the reasons are not well understood. We propose a dual channel view of code; code combines a formal channel for specifying execution and a natural language channel in the form of identifiers and comments that assists human comprehension. Computers ignore the natural language channel, but developers read both and, when writing code for longterm use and maintenance, consider each channel's audience: computer and human. As developers hold both channels in mind when coding, we posit that the two channels interact and constrain each other; we call these dual channel constraints. Their impact has been neglected. We describe how they can lead to humans writing code in a way more predictable than natural language, highlight pioneering research that has implicitly or explicitly used parts of this theory, and drive new research, such as systematically searching for cross-channel inconsistencies. Dual channel constraints provide an exciting opportunity as truly multi-disciplinary research; for computer scientists they promise improvements to program analysis via a more holistic approach to code, and to psycholinguists they promise a novel environment for studying linguistic processes.
SAShA: Semantic-Aware Shilling Attacks on Recommender Systems Exploiting Knowledge Graphs

Recommender systems (RS) play a focal position in modern user-centric online services. Among them, collaborative filtering (CF) approaches have shown leading accuracy performance compared to content-based filtering (CBF) methods. Their success is due to an effective exploitation of similarities/correlations encoded in user interaction patterns, which is computed by considering common items users rated in the past. However, their strength is also their weakness. Indeed, a malicious agent can alter recommendations by adding fake user profiles into the platform thereby altering the actual similarity values in an engineered way. The spread of well-curated information available in knowledge graphs ($$\mathcal {KG}$$) has opened the door to several new possibilities in compromising the security of a recommender system. In fact, $$\mathcal {KG}$$ are a wealthy source of information that can dramatically increase the attacker’s (and the defender’s) knowledge of the underlying system. In this paper, we introduce SAShA, a new attack strategy that leverages semantic features extracted from a knowledge graph in order to strengthen the efficacy of the attack to standard CF models. We performed an extensive experimental evaluation in order to investigate whether SAShA is more effective than baseline attacks against CF models by taking into account the impact of various semantic features. Experimental results on two real-world datasets show the usefulness of our strategy in favor of attacker’s capacity in attacking CF models.
KoGuN: Accelerating Deep Reinforcement Learning via Integrating Human Suboptimal Knowledge

Reinforcement learning agents usually learn from scratch, which requires a large number of interactions with the environment. This is quite different from the learning process of human. When faced with a new task, human naturally have the common sense and use the prior knowledge to derive an initial policy and guide the learning process afterwards. Although the prior knowledge may be not fully applicable to the new task, the learning process is significantly sped up since the initial policy ensures a quick-start of learning and intermediate guidance allows to avoid unnecessary exploration. Taking this inspiration, we propose knowledge guided policy network (KoGuN), a novel framework that combines human prior suboptimal knowledge with reinforcement learning. Our framework consists of a fuzzy rule controller to represent human knowledge and a refine module to finetune suboptimal prior knowledge. The proposed framework is end-to-end and can be combined with existing policy-based reinforcement learning algorithm. We conduct experiments on several control tasks. The empirical results show that our approach, which combines suboptimal human knowledge and RL, achieves significant improvement on learning efficiency of flat RL algorithms, even with very low-performance human prior knowledge.
Real-Time Detection of Fake Account in Twitter Using Machine-Learning Approach

In day-to-day life for sharing of information and communication among others, online social networking has emerged as one of the eminent ways. Online social networking offers attractive means of social interaction and communication but it raises various concerns like privacy and security of user and its account. During the events, people share unreliable information and misleading content through social media platform such as Twitter and Facebook. Our objective is to detect the fake account by analyzing different characteristics that spread malicious contents in real-time environment. Fake profiles are the identity theft of genuine user’s profile content and create a similar profile using users credential. At later stage, the profile is perverted for aspersing the legitimate profile owner as well as sending friend request to the user’s friend. In this article, we discuss about our chrome extension-based framework that detects the fake accounts in Twitter environment by analyzing the different characteristics.
Emojis influence autobiographical memory retrieval from reading words: An fMRI-based study

Advances in computer and communications technology have deeply affected the way we communicate. Social media have emerged as a major means of human communication. However, a major limitation in such media is the lack of non-verbal stimuli, which sometimes hinders the understanding of the message, and in particular the associated emotional content. In an effort to compensate for this, people started to use emoticons, which are combinations of keyboard characters that resemble facial expressions, and more recently their evolution: emojis, namely, small colorful images that resemble faces, actions and daily life objects. This paper presents evidence of the effect of emojis on memory retrieval through a functional Magnetic Resonance Imaging (fMRI) study. A total number of fifteen healthy volunteers were recruited for the experiment, during which successive stimuli were presented, containing words with intense emotional content combined with emojis, either with congruent or incongruent emotional content. Volunteers were asked to recall a memory related to the stimulus. The study of the reaction times showed that emotional incongruity among word+emoji combinations led to longer reaction times in memory retrieval compared to congruent combinations. General Linear Model (GLM) and Blind Source Separation (BSS) methods have been tested in assessing the influence of the emojis on the process of memory retrieval. The analysis of the fMRI data showed that emotional incongruity among word+emoji combinations activated the Broca's area (BA44 and BA45) in both hemispheres, the Supplementary Motor Area (SMA) and the inferior prefrontal cortex (BA47), compared to congruent combinations. Furthermore, compared to pseudowords, word+emoji combinations activated the left Broca's area (BA44 and BA45), the amygdala, the right temporal pole (BA48) and several frontal regions including the SMA and the inferior prefrontal cortex.
Internal Evaluation of Unsupervised Outlier Detection

Although there is a large and growing literature that tackles the unsupervised outlier detection problem, the unsupervised evaluation of outlier detection results is still virtually untouched in the literature. The so-called internal evaluation, based solely on the data and the assessed solutions themselves, is required if one wants to statistically validate (in absolute terms) or just compare (in relative terms) the solutions provided by different algorithms or by different parameterizations of a given algorithm in the absence of labeled data. However, in contrast to unsupervised cluster analysis, where indexes for internal evaluation and validation of clustering solutions have been conceived and shown to be very useful, in the outlier detection domain, this problem has been notably overlooked. Here we discuss this problem and provide a solution for the internal evaluation of outlier detection results. Specifically, we describe an index called Internal, Relative Evaluation of Outlier Solutions (IREOS) that can evaluate and compare different candidate outlier detection solutions. Initially, the index is designed to evaluate binary solutions only, referred to as top - n outlier detection results. We then extend IREOS to the general case of non-binary solutions, consisting of outlier detection scorings. We also statistically adjust IREOS for chance and extensively evaluate it in several experiments involving different collections of synthetic and real datasets.
Valuing free-form text data from maintenance logs through transfer learning with CamemBERT

Coupling a production scheduling process with maintenance logs can provide important advantages. For instance, this enables the adaptation of planning to the reality of the shop floor. Nevertheless, maintenance logs are often highly unstructured, as they mainly rely on free-form text comments from operators, and are imbalanced, as commonplace issues happen more often than critical problems. This hinders the application of machine learning methods to exploit this data. Thus, this study explores the use of a recent model named CamemBERT to tackle these difficulties through transfer learning. More specifically, the purpose is to predict the criticality and duration of a maintenance issue from the description provided. Findings suggest that fine-tuning CamemBERT outperforms other classical and feature-based approaches. Furthermore, the class imbalance problem is addressed from a data pre-processing and training perspective: firstly, k-means with silhouette diagrams allowed the creation of more homogenous classes, and secondly, the use of resampling enabled an improvement in the model’s performance.
The shape of breast cancer

Little is known about the three-dimensional shape of breast cancer. Implicit to approaches that localize the center of the tumor for breast-conserving surgery (BCS) of non-palpable cancers is the assumption that breast cancers are spherical about a central point, which may not be accurate. Pre-operative supine breast MRI images were obtained of 83 breast cancer patients undergoing partial mastectomy using supine MRI-guided resection techniques. Three-dimensional (3D) tumor models were derived after radiologists outlined tumor edges on successive MRI slices. Ideal resection volumes were determined by adding 1 cm in every dimension to the actual tumor volume. Geometrically defined parameters were used to define tumor shapes and associations between clinical variables and shapes were examined. Seventy-five patients had invasive cancer. Breast cancers were categorized into four tumor shapes: 34% of tumors were discoidal, 29% segmental, 19% spherical, and 18% irregular. If hypothetical spherical excisions were performed, non-spherical cases would excise 143% more tissue than the ideal resection volume. When the 3D shape of each tumor was provided to the surgeon during MR-guided BCS, the percentage of tissue overexcised in non-spherical cases was significantly less (143% vs. 66%, p < 0.001). Information obtained from a supine MRI can be used to generate 3D tumor models and rapidly classify breast tumor shapes. The vast majority of invasive cancers and DCIS are not spherical. Knowledge of tumor shape may allow surgeons to excise breast cancer more precisely.
Automatic, location-privacy preserving dashcam video sharing using blockchain and deep learning

Abstract Today, many people use dashcams, and videos recorded on dashcams are often used as evidence of accident fault. People can upload videos of dashcam recordings with specific accident clips and share the videos with others who request them, by providing the time or location of an accident. However, dashcam videos are erased when the dashcam memory is full, so periodic backup is necessary for video sharing. It is inconvenient for dashcam owners to search for and transmit a requested video clip from backup videos. In addition, anonymity is not ensured, which may reduce location privacy by exposing the video owner’s location. To solve this problem, we propose a video sharing scheme with accident detection using deep learning coupled with automatic transfer to the cloud; we also propose ensuring data and operational integrity along with location privacy by using blockchain smart contracts. Furthermore, our proposed system uses proxy re-encryption to enhance the confidentiality of a shared video. Our experiments show that our proposed automatic video sharing system is cost-effective enough to be acceptable for deployment.
Security analysis of measurement-device-independent quantum secure direct communication

Quantum secure direct communication (QSDC) is an important branch of quantum communication that transmits confidential messages directly in a quantum channel without utilizing encryption and decryption. It not only prevents eavesdropping during transmission, but also eliminates the security loophole associated with key storage and management. Recently measurement-device-independent (MDI) QSDC protocols in which the measurement is performed by an untrusted party using imperfect measurement devices have been constructed, and MDI-QSDC eliminates the security loopholes originating from the imperfections in measurement devices so that enable applications of QSDC with current technology. In this paper, we complete the quantitative security analysis of the MDI-QSDC protocols, one based on EPR pairs and one based on single photons. In passing, a security loophole in one of the MDI-QSDC protocols (Niu et al. in Sci Bull 63(20):1345–1350, 2018) is fixed. The security capacity is derived, and its lower bound is given. It is found that the MDI-QSDC secrecy capacity is only slightly lower than that of QSDC utilizing perfect measurement devices. Therefore, QSDC is possible with current measurement devices by sacrificing a small amount in the capacity.
Formal Analysis and Implementation of a TPM 2.0-based Direct Anonymous Attestation Scheme

Direct Anonymous Attestation (Daa) is a set of cryptographic schemes used to create anonymous digital signatures. To provide additional assurance, Daa schemes can utilise a Trusted Platform Module (Tpm) that is a tamper-resistant hardware device embedded in a computing platform and which provides cryptographic primitives and secure storage. We extend Chen and Li's Daa scheme to support: 1) signing a message anonymously, 2) self-certifying Tpm keys, and 3) ascertaining a platform's state as recorded by the Tpm's platform configuration registers (PCR) for remote attestation, with explicit reference to Tpm2.0 API calls. We perform a formal analysis of the scheme and are the first symbolic models to explicitly include the low-level Tpm call details. Our analysis reveals that a fix pro-posed by Whitefield et al. to address an authentication attack on an Ecc-Daa scheme is also required by our scheme. Developing a fine-grained, formal model of a Daa scheme contributes to the growing body of work demonstrating the use of formal tools in supporting security analyses of cryptographic protocols. We additionally provide and benchmark an open-source C++implementation of this Daa scheme supporting both a hardware and a software Tpm and measure its performance.
Critique Me

Creative workers frequently turn to online critique communities for feedback on their work. While past research has focused primarily on how to yield better feedback from providers, less is known about the strategies feedback seekers use to engage providers and request feedback. We present two studies to explore the feedback exchange dynamics between feedback requesters and providers in the subreddit community, r/design\_critiques. In Study 1, we interviewed 12 community members and found that while creators have strategies to request feedback, they expressed uncertainty about whether and how to include details about the design context, personal background, and specific feedback needs. In Study 2, through a mixed-method analysis, we identified how specific request strategies impact the quantity and quality of community feedback, and found several key, but undervalued strategies: signaling as a novice, critiquing one's own design, and providing design variants. These strategies led to better community response, but were rarely used. We offer design implications around how to leverage these insights to improve online feedback exchange
Distant Supervision in BERT-based Adhoc Document Retrieval

Recently introduced pre-trained contextualized autoregressive models like BERT have shown improvements in document retrieval tasks. One of the major limitations of the current approaches can be attributed to the manner they deal with variable-size document lengths using a fixed input BERT model. Common approaches either truncate or split longer documents into small sentences/passages and subsequently label them - using the original document label or from another externally trained model. The other problem is the scarcity of labelled query-document pairs that directly hampers the performance of modern data hungry neural models. This process gets even more complicated with the partially labelled large dataset of queries derived from query logs (TREC-DL). In this paper, we handle both the issues simultaneously and introduce passage level weak supervision in contrast to standard document level supervision. We conduct a preliminary study on the document to passage label transfer and influence of unlabelled documents on the performance of adhoc document retrieval. We observe that direct transfer of relevance labels from documents to passages introduces label noise that strongly affects retrieval effectiveness. We propose a weak-supervision based transfer passage labelling scheme that helps in performance improvement and gathering relevant passages from unlabelled documents.
Improvement of Face and Eye Detection Performance by Using Multi-task Cascaded Convolutional Networks

Detection of face and eyes in unrestricted conditions has been a problem for years due to various expressions, illumination, and color fringing. Recent studies show that deep learning methods can attain impressive performance in the identification of different objects and patterns. As various systems may use the human face as input material, the increase in facial and eye detection performance has some significance. This paper introduces an enhanced face and eye detection technique through the use of cascaded multi-task convolutional networks for our dataset. We propose in this paper a deep cascaded multi-task system that exploits their inherent correlation to improve their performance. We collected 100 videos containing about 18265 images captured from our device and applied this dataset to the process and other systems proposed. The educated model was checked on our dataset and contrasted with the Haar cascade model as well. Our proposed method achieves a 98% percent accuracy rate considering our dataset which is superior to the other techniques used to detect the face and eye from an image. Besides, this paper also reflects a study of different methods of detecting the eye and face in tabular format from videos. The experimental results however indicate that the proposed approach demonstrates enhanced eye and face detection output from videos.
Document Classification for COVID-19 Literature

The global pandemic has made it more important than ever to quickly and accurately retrieve relevant scientific literature for effective consumption by researchers in a wide range of fields.We provide an analysis of several multi-label document classification models on the LitCovid dataset, a growing collection of 23,000 research papers regarding the novel 2019 coronavirus.We find that pre-trained language models fine-tuned on this dataset outperform all other baselines and that BioBERT surpasses the others by a small margin with micro-F1 and accuracy scores of around 86% and 75% respectively on the test set.We evaluate the data efficiency and generalizability of these models as essential features of any system prepared to deal with an urgent situation like the current health crisis.We perform a data ablation study to determine how important article titles are for achieving reasonable performance on this dataset.Finally, we explore 50 errors made by the best performing models on LitCovid documents and find that they often (1) correlate certain labels too closely together and (2) fail to focus on discriminative sections of the articles; both of which are important issues to address in future work.Both data and code are available on GitHub 1 .