Reviewerly/exHarmony huggingface dataset:
Triple-Layer Image Security Using a Zigzag Embedding Pattern

This paper proposes a triple-layer, high capacity, message security scheme. The first two layers are of a cryptographic nature, whereas the third layer is of a steganographic nature. In the first layer, AES-128 encryption is performed on the secret message. In the second layer, a chaotic logistic map encryption is applied on the output of the first secure layer to increase the security of the scheme. In the third layer of security, a 2D image steganography technique is performed, where the least significant bit (LSB) -embedding is done according to a zigzag pattern in each of the three color planes of the cover image (i.e. RGB). The distinguishing feature of the proposed scheme is that the secret data is hidden in a zigzag manner that cannot be predicted by a third party. Moreover, our scheme achieves higher values of peak signal to noise ratio (PPSNR), mean square error (MSE), the structural similarity index metric (SSIM), normal cross correlation (NCC) and image fidelity (IF) compared to its counterparts form the literature. In addition, a histogram analysis as well as the high achieved capacity are magnificent indicators for a reliable and high capacity steganographic scheme.
Spatial–Temporal Context-Aware Online Action Detection and Prediction

Spatial-temporal action detection in videos is a challenging problem that has attracted considerable attention in recent years. Most current approaches address action detection as an object detection problem, which utilizes successful object detection frameworks such as Faster R-CNN to operate action detection at every single frame first, and then generates action tubes by linking bounding boxes across the whole video in an offline fashion. However, unlike object detection in static images, temporal context information is vital for action detection in videos. Therefore, we propose an online action detection model that leverages the spatial-temporal context information existing in videos to perform action inference and localization. More specifically, we try to depict the spatial-temporal context pattern of actions via an encoder-decoder model that is based on a convolutional recurrent neural network. The model accepts a video snippet as input and encodes the dynamic information inside the snippet in the forward pass. During the backward pass, the decoder resolves the information for action detection with the current appearance or motion cue at each time stamp. In addition, we devise an incremental action-tube construction algorithm that enables our model to accomplish action prediction ahead of time and performs action detection in an online fashion. To evaluate the performance of our method, we conduct experiments on three popular public datasets UCF-101, UCF-Sports, and J-HMDB-21. The experimental results demonstrate that our method can achieve competitive or superior performance when compared to the state-of-the-art methods. To encourage further research, we release our project on “https://github.com.hjjpku.OATD.”
Relation Classification via Keyword-Attentive Sentence Mechanism and Synthetic Stimulation Loss

Previous studies have shown that attention mechanisms and shortest dependency paths have a positive effect on relation classification. In this paper, a keyword-attentive sentence mechanism is proposed to effectively combine the two methods. Furthermore, to effectively handle the imbalanced classification problem, this paper proposes a new loss function called the synthetic stimulation loss, which uses a modulating factor to allow the model to focus on hard-to-classify samples. The proposed two methods are integrated into a bidirectional gated recurrent unit (BiGRU). As a single model is not strong in noise immunity, this paper applies the mutual learning method to our model and forces the networks to teach each other. Therefore, we call the final model SSL-KAS-MuBiGRU. Experiments on the SemEval-2010 Task 8 data set and the TAC40 data set demonstrate that the keyword-attentive sentence mechanism and synthetic stimulation loss are useful for relation classification, and our model achieves state-of-the-art results.
Adaptive Learning Material Recommendation in Online Language Education

In online language education, it is challenging to recommend learning materials that match the student's knowledge since we typically lack information about the difficulty of materials and the abilities of each student. We propose a refined hierarchical structure to model vocabulary knowledge in a corpus and introduce an adaptive algorithm to recommend reading texts for online language learners. We evaluated our approach with a Japanese learning tool, finding that adding adaptivity into material recommendation significantly increased engagement.
Insecurity and Hardness of Nearest Neighbor Queries Over Encrypted Data

Nearest neighbor query processing is a fundamental problem that arises in many fields such as spatial databases and machine learning. ASPE, which uses invertible matrices to encrypt data, is a widely adopted Secure Nearest Neighbor (SNN) query scheme. Encrypting data by matrices is actually a linear combination of the multiple dimensions of the data, which is completely consistent with the relationship between the source signals and observed signals in the signal processing. By viewing dimensions of the data and the encrypted data as source signals and observed signals, respectively, we formally prove and experimentally demonstrate that ASPE is actually insecure against even ciphertext only attacks, using signal processing theory. Prior work proved that it is impossible to construct an SNN scheme even in much relaxed standard security models, we invalidate this hardness understanding by pointing out the incorrectness of the hardness proof.
Blockchain-Based Badge Award with Existence Proof

In this paper, we present a badge awarding system for performance assessment in education using blockchain technology. Learners will be awarded badges, which are certified for a predetermined level of progress in terms of learning. All the badges are stored in a backpack, which is an environment for storing and presenting the obtained badges. Badges are immutable and verifiable as well as rigid. The use of badges in the education system makes education more interesting, skill-based, and adaptable to changes. The key contribution of our work is in terms of compatibility with Openbadge specification and integrated cooperating platform between digital badge awarding and blockchaining. Our system implementation is compatible with Open Badges of IMS Global Learning Consortium, which is used to earn, issue, and award badges across various platforms. The badges are trusted by the IMS standard, the criteria to earn a badge is verified through the network, and the overall process is transparent compared to the traditional education system. Moreover, all badge awarding events in our system are recorded in a blockchain. Once stored, the contents cannot be tampered with. Thereafter, anyone can check the validity of the badge through the blockchain. Our platform will be useful for distance learning as well as time and location independent learning. The experimental results are as follows. On a Bitcoin-based digital badge publishing platform, the execution time required to award the badge is 24.53 s, while on the Ethereum-based digital badge publishing platform, the execution time to award the badge is only 3.86 s. It can also be used for career management and personal history. The learners can obtain the current knowledge required for a job. Lifelong learning will be also possible with this platform.
A Quality-Aware Web API Recommender System for Mashup Development

The rapid increase in the number and diversity of web APIs with similar functionality, makes it challenging to find suitable ones for mashup development. In order to reduce the number of similarly functional web APIs, recommender systems are used. Various web API recommendation methods exist which attempt to improve recommendation accuracy, by mainly using some discovered relationships between web APIs and mashups. Such methods are basically incapable of recommending quality web APIs because they fail to incorporate web API quality in their recommender systems. In this work, we propose a method that considers the quality features of web APIs, to make quality web API recommendations. Our proposed method uses web API quality to estimate their relevance for recommendation. Specifically, we propose a matrix factorization method, with quality feature regularization, to make quality web API recommendations and also enhance recommendation diversity. We demonstrate the effectiveness of our method by conducting experiments on a real-world dataset from www.programmableweb.com. Our results not only show quality web API recommendations, but also, improved recommendation accuracy. In addition, our proposed method improves recommendation diversity by mitigating the negative Matthew effect of accumulated advantage, intrinsic to most existing web API recommender systems. We also compare our method with some baseline recommendation methods for validation.
Novel Bit-Parallel and Digit-Serial Systolic Finite Field Multipliers Over $GF(2^m)$ Based on Reordered Normal Basis

Efficient implementation of finite field multipliers based on a reordered normal basis (RNB) is highly desirable in the current/emerging cryptosystems since it offers almost free realization of squaring operation. Therefore, in this paper, we propose novel bit-parallel and digit-serial finite field multipliers over GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) based on RNB. By efficient transformation of the core multiplication algorithm using a unique circular shifting feature, we have derived an efficient algorithm for low-complexity systolic mapping. Both bit-parallel and digit-serial structures of the multipliers are then obtained and optimized to enhance the area-time efficiency. We have also utilized the unique feature of the proposed multiplication algorithm to obtain the systolic multipliers by Karatsubalike decomposition. Detailed analysis and comparison show the superior performance of the proposed implementation. For example, the proposed regular and Karatsuba-based bit-parallel designs involve at least 48.4% less area-delay product (ADP) and 42.2% less power-delay product (PDP) than the best existing ones (37.7% and 55.3% less ADP and PDP on field-programmable gate array platform), respectively. The proposed multipliers, because of their lower area-time complexities, can be used for efficient realization of cryptographic applications.
XPC

Microkernel has many intriguing features like security, fault-tolerance, modularity and customizability, which recently stimulate a resurgent interest in both academia and industry (including seL4, QNX and Google's Fuchsia OS). However, IPC (inter-process communication), which is known as the Achilles' Heel of microkernels, is still the major factor for the overall (poor) OS performance. Besides, IPC also plays a vital role in monolithic kernels like Android Linux, as mobile applications frequently communicate with plenty of user-level services through IPC. Previous software optimizations of IPC usually cannot bypass the kernel which is responsible for domain switching and message copying/remapping; hardware solutions like tagged memory or capability replace page tables for isolation, but usually require non-trivial modification to existing software stack to adapt the new hardware primitives. In this paper, we propose a hardware-assisted OS primitive, XPC (Cross Process Call), for fast and secure synchronous IPC. XPC enables direct switch between IPC caller and callee without trapping into the kernel, and supports message passing across multiple processes through the invocation chain without copying. The primitive is compatible with the traditional address space based isolation mechanism and can be easily integrated into existing microkernels and monolithic kernels. We have implemented a prototype of XPC based on a Rocket RISC-V core with FPGA boards and ported two microkernel implementations, seL4 and Zircon, and one monolithic kernel implementation, Android Binder, for evaluation. We also implement XPC on GEM5 simulator to validate the generality. The result shows that XPC can reduce IPC call latency from 664 to 21 cycles, up to 54.2x improvement on Android Binder, and improve the performance of real-world applications on microkernels by 1.6x on Sqlite3 and 10x on an HTTP server with minimal hardware resource cost.
Functional broadcast encryption with applications to data sharing for cloud storage

Cloud storage services provide data owners an efficient and flexible way to share data. Among the shared data, some of them are very sensitive, and should be prevented for any leakage. Should users conventionally encrypt the data, however, flexibly sharing is lost. Public-key encryption with access control (PEAC) resolves this tension. Most of existing PEAC schemes only support the data owner to control either the parts of data to be accessed by other users (file-based PEAC), or the membership of users that access the entire data set (receiver-based PEAC). However, in reality a PEAC scheme with both file-based and receiver-based functionalities is required to ensure the efficiency, flexibility, and fine-grainess of the data sharing service. In this paper, we introduce a primitive of functional broadcast encryption (FBE). FBE is a manifestation of PEAC that enables a data owner to share a set of data files to a group of users, where only a specific subset of data files can be accessed and decrypted by a specific subgroup of users. We describe a construction for FBE based on indistinguishability obfuscation (iO). Security analysis demonstrates that the proposed scheme achieves selective IND-CCA security, and a comprehensive performance analysis shows the proposed scheme is efficient.
Being Yourself to be Creative

Creative idea generation, the generation of original yet effective ideas, can be supported in virtual environments when interactions are facilitated via avatars with an appearance similar to their user, i.e. self-similar avatars. However, it is not known how self-similar avatars support creative idea generation. We propose that self-similarity supports the generation of original ideas, because it (i) increases the identification a user has with its avatar, which (ii) increases positive affect, and (iii) influences the positive affect-original idea generation link. To test this conjecture, an experiment was conducted where people composed their own avatar to be either self-similar or non-self-similar, which they then used to engage in two idea generation tasks presented within a custom virtual environment. The results suggest that using a self-similar rather than a non-self-similar avatar positively influences the generation of original ideas; and that this depends on the influence of self-similarity on the link between identification and positive affect. Thus, this paper contributes a mechanism that explains how self-similar avatars support the generation of original ideas.
Integrating Development and Operations in Cross-Functional Teams - Toward a DevOps Competency Model

The integration of cross-functional teams for new product development is still an elusive aim. Cross-functional information technology (IT) teams are used to provide new initiatives in fast-changing and challenging environments. Moreover, concepts such as Development and Operations (DevOps) appear in practice and bring software development and operations tasks in one team. Organizations are currently searching for necessary, suitable competencies for setting up high collaborative cross-functional teams that manage the tasks of the software delivery lifecycle. Therefore, in this paper, employing a multi-perspective research approach, we conducted a workshop and a multiple-case study. Hence, this paper presents a competency model for enabling a high level of collaboration within a team and explains how these competencies are implemented in IT functions. Additionally, we identified six competencies and two major challenges associated with DevOps team setups.
Canonicalization of Open Knowledge Bases with Side Information from the Source Text

Nowadays Open Information Extraction (Open IE) approaches, which extract <;noun phrase, relation phrase, noun phrase> triples from unstructured text, contribute to the construction of large Open Knowledge Bases (Open KBs). However, one crucial problem is that the noun phrases and relation phrases in the extracted triples are not well canonicalized, which leads to a large number of redundant and ambiguous facts. For example, both <;Barack Obama, was born in, Honolulu> and <;President Obama, has birthplace, Honolulu> may be extracted and stored in Open KBs. Recent research proposes to solve this problem by clustering over manually-defined feature spaces based on the similarity of the noun phrases and relation phrases. However, the performance of such techniques is limited, since only the information contained in the triples is utilized to measure their similarity. In this paper, we propose to perform canonicalization over Open IE triples by incorporating the side information from the original data sources, including the candidate entities of the noun phrases detected in the source text, the types of the candidate entities and the domain knowledge of the source text. We model the canonicalization problem of noun phrases and relation phrases jointly based on such side information, and demonstrate the effectiveness of our approach through extensive experiments on two real-world datasets.
Ultrafast Object Detection in Naturalistic Vision Relies on Ultrafast Distractor Suppression

People are quicker to detect examples of real-world object categories in natural scenes than is predicted by classic attention theories. One explanation for this puzzle suggests that experience renders the visual system sensitive to midlevel features diagnosing target presence. These are detected without the need for spatial attention, much as occurs for targets defined by low-level features like color or orientation. The alternative is that naturalistic search relies on spatial attention but is highly efficient because global scene information can be used to quickly reject nontarget objects and locations. Here, we use ERPs to differentiate between these possibilities. Results show that hallmark evidence of ultrafast target detection in frontal brain activity is preceded by an index of spatially specific distractor suppression in visual cortex. Naturalistic search for heterogenous targets therefore appears to rely on spatial operations that act on neural object representations, as predicted by classic attention theory. People appear able to rapidly reject nontarget objects and locations, consistent with the idea that global scene information is used to constrain naturalistic search and increase search efficiency.
QoE for Mobile Clients with Segment-aware Rate Adaptation Algorithm (SARA) for DASH Video Streaming

Dynamic adaptive streaming over HTTP (DASH) is widely used for video streaming on mobile devices. Ensuring a good quality of experience (QoE) for mobile video streaming is essential, as it severely impacts both the network and content providers’ revenue. Thus, a good rate adaptation algorithm at the client end that provides high QoE is critically important. Recently, a segment size-aware rate adaptation (SARA) algorithm was proposed for DASH clients. However, its performance on mobile clients has not been investigated so far. The main contributions of this article are twofold: (1) We discuss SARA’s implementation for mobile clients to improve the QoE in mobile video streaming, one that accurately predicts the download time for the next segment and makes an informed bitrate selection, and (2) we developed a new parametric QoE model to compute a cumulative score that helps in fair comparison of different adaptation algorithms. Based on our subjective and objective evaluation, we observed that SARA for mobile clients outperforms others by 17% on average, in terms of the Mean Opinion Score, while achieving, on average, a 76% improvement in terms of the interruption ratio. The score obtained from our new parametric QoE model also demonstrates that the SARA algorithm for mobile clients gives a better QoE among all the algorithms.
Reconstruction of an early viola da gamba informed by physical modeling

The reconstruction of an early viola da gamba is considered, using virtual prototyping by means of the finite element method. Based on iconographic sources, previous research has postulated an instrument design lacking a soundpost and a bass bar. This led to the hypothesis of a top plate with variable thickness. In order to investigate the acoustic efficiency of such a design, a finite element model of the instrument is formulated. The structural accuracy of the model is qualitatively verified by comparing calculated modal shapes with those of a reconstructed instrument, visualized with the aid of Chladni patterns and electronic speckle pattern interferometry. Furthermore, simulating the interaction between the vibrating surfaces of the instrument and the surrounding air shows that the posited asymmetric design can radiate sound more efficiently than a design involving a symmetric top plate. However, the asymmetry introduced by the gradually thickening top plate is weaker than that usually enforced by the presence of a soundpost and a bass bar. Therefore, low frequency structural modes of the instrument are less easily excited by a force acting parallel to the top plate.
Supervised Classification of Histopathological Images Using Convolutional Neuronal Networks for Gastric Cancer Detection

This paper presents a pilot-test for gastric cancer detection using histopathological image samples taken from pathology laboratory of the Universidad Industrial de Santander. The proposal follows two approaches: the first one analyzes the image as a whole to find characteristic properties of the benign samples, such as, the correct organization of the cells to form glands; the second one considers local morphological features, such as, the size, the shape, and the intensity of the light of individual nuclei in the cells. For both approaches, the automatic detection is based on a supervised classification model using a literature deep convolutional neural network. Experiments show a detection accuracy of up to 89,72%, which indicates that the proposal is a promising tool to assist the pathological diagnosis.
Predicting Patient’s Diagnoses and Diagnostic Categories from Clinical-Events in EHR Data

In this paper we develop and study machine learning based models based on latent semantic indexing capable of automatically assigning diagnoses and diagnostic categories to patients based on structured clinical data in their Electronic Health record (EHR). These models can be either used for automatic coding of patient's diagnoses from structured EHR data at the time of discharge, or for supporting dynamic diagnosis and summarization of the patient condition. We study the performance of our diagnostic models on MIMIC-III EHR data.