Reviewerly/exHarmony huggingface dataset:
Automatic Search of Meet-in-the-Middle Preimage Attacks on AES-like Hashing

The Meet-in-the-Middle (MITM) preimage attack is highly effective in breaking the preimage resistance of many hash functions, including but not limited to the full MD5, HAVAL, and Tiger, and reduced SHA-0/1/2. It was also shown to be a threat to hash functions built on block ciphers like AES by Sasaki in 2011. Recently, such attacks on AES hashing modes evolved from merely using the freedom of choosing the internal state to also exploiting the freedom of choosing the message state. However, detecting such attacks especially those evolved variants is difficult. In previous works, the search space of the configurations of such attacks is limited, such that manual analysis is practical, which results in sub-optimal solutions. In this paper, we remove artificial limitations in previous works, formulate the essential ideas of the construction of the attack in well-defined ways, and translate the problem of searching for the best attacks into optimization problems under constraints in Mixed-Integer-Linear-Programming (MILP) models. The MILP models capture a large solution space of valid attacks; and the objectives of the MILP models are attack configurations with the minimized computational complexity. With such MILP models and using the off-the-shelf solver, it is efficient to search for the best attacks exhaustively. As a result, we obtain the first attacks against the full (5-round) and an extended (5.5-round) version of Haraka-512 v2, and 8-round AES-128 hashing modes, as well as improved attacks covering more rounds of Haraka-256 v2 and other members of AES and Rijndael hashing modes.
Exploration of critical care data by using unsupervised machine learning

Identification of subgroups may be useful to understand the clinical characteristics of ICU patients. The purposes of this study were to apply an unsupervised machine learning method to ICU patient data to discover subgroups among them; and to examine their clinical characteristics, therapeutic procedures conducted during the ICU stay, and discharge dispositions. K-means clustering method was used with 1503 observations and 9 types of laboratory test results as features. Three clusters were identified from this specific population. Blood urea nitrogen, creatinine, potassium, hemoglobin, and red blood cell were distinctive between the clusters. Cluster Three presented the highest blood products transfusion rate (19.8%), followed by Cluster One (15.5%) and cluster Two (9.3%), which was significantly different. Hemodialysis was more frequently provided to Cluster Three while bronchoscopy was done to Cluster One and Two. Cluster Three showed the highest mortality (30.4%), which was more than two-fold compared to Cluster One (14.1%) and Two (12.2%). Three subgroups were identified and their clinical characteristics were compared. These findings may be useful to anticipate treatment strategies and probable outcomes of ICU patients. Unsupervised machine learning may enable ICU multi-dimensional data to be organized and to make sense of the data.
Automated diagnosis of multi-plane breast ultrasonography images using deep neural networks

Breast ultrasonography is currently considered the first-line examination in the diagnosis of breast lesions or other abnormalities. Many automated breast cancer diagnosis methods have been developed for ultrasonography images; however, most previous automated methods used only a single breast ultrasonography image. This is inconsistent with the real-world situation, as breast cancer is heterogeneous, and it can lead to high false negative rates. Generally, sonographers diagnose a lesion by reviewing multiple planes. In this paper, we formulate the diagnosis of breast cancer on ultrasonography images as a Multiple Instance Learning (MIL) problem, diagnosing a breast nodule by jointly analyzing the nodule on multiple planes. An attention-augmented deep neural network is then developed to solve this problem. To the best of our knowledge, this is the first implementation of a MIL framework on such data. A large breast ultrasonography image dataset was constructed to train and evaluate the model; this contained 10,464 images from 3700 lesions labeled as benign or malignant, which originated from 2568 patients. The high classification accuracy achieved demonstrates the effectiveness of the proposed architecture for the diagnosis of breast cancer. The MIL based method obtained superior performance to single instance methods in this breast cancer diagnosis task. Notably, the proposed attention-augmented network allowed us to find key instances, which can be provided the region of interest (ROI) in the final diagnosis given to a doctor. Furthermore, we empirically demonstrate that our approach achieves better performance than other state-of-the-art MIL methods.
Explainable AI in Industry: Practical Challenges and Lessons Learned

Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences. Moreover, with the proliferation of AI based solutions in areas such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI have become far-reaching. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias in these models, and a demand for model transparency and interpretability [11]. Model explainability is considered a prerequisite for building trust and adoption of AI systems in high stakes domains such as lending and healthcare [1] which require reliability, safety, and fairness. It is also critical to automated transportation, and other industrial applications with significant socio-economic implications such as predictive maintenance, exploration of natural resources, and climate change modeling.
Improving resource utilization by timely fine-grained scheduling

Monotask is a unit of work that uses only a single type of resource (e.g., CPU, network, disk I/O). While monotask was primarily introduced as a means to reason about job performance, in this paper we show that this fine-grained, resource-oriented abstraction can be leveraged by job schedulers to maximize cluster resource utilization. Although recent cluster schedulers have significantly improved resource allocation, the utilization of the allocated resources is often not high due to inaccurate resource requests. In particular, we show that existing scheduling mechanisms are ineffective for handling jobs with dynamic resource usage, which exists in common workloads, and propose a resource negotiation mechanism between job schedulers and executors that makes use of monotasks. We design a new framework, called Ursa, which enables the scheduler to capture accurate resource demands dynamically from the execution runtime and to provide timely, fine-grained resource allocation based on monotasks. Ursa also enables high utilization of the allocated resources by the execution runtime. We show by experiments that Ursa is able to improve cluster resource utilization, which effectively translates to improved makespan and average JCT.
Separable Reversible Data Hiding Scheme in Homomorphic Encrypted Domain Based on NTRU

NTRU (Number Theory Research Unit) has the characteristics of resistance to quantum computing attacks, fast encryption and decryption, and high security. It is very suitable for wireless confidential data networks and authentication systems. Combined with reversible data hiding technology, a separable reversible data hiding scheme in homomorphic encrypted domain based on NTRU is proposed. The image owner directly divides the cover-image into groups of a reference pixel and T adjacent pixels. Then, the grouped image is encrypted by NTRU. Finally, the encrypted image is uploaded to the data hider. The encrypted image is divided into groups by the same grouping method as the image owner. After that, the data hider calculates T absolute differences of adjacent pixels in each group to obtain histogram of the absolute differences. The additional data can be embedded into the encrypted image by shifting histogram of the absolute differences. After receiving the encrypted image with hidden data, the receiver can either use the data hiding key to directly extract the additional data from encrypted domain to obtain the encrypted image, or use the private key and data hiding key to extract the additional data from plaintext domain to get the cover-image. Experimental results show that our scheme has higher security and better embedding performance comparing with other state-of-the-art works.
A new re-encoding ECOC using reject option

Abstract When training base classifier by ternary Error Correcting Output Codes (ECOC), it is well know that some classes are ignored. On this account, a non-competent classifier emerges when it classify an instance whose real label does not belong to the meta-subclasses. Meanwhile, the classic ECOC dichotomizers can only produce binary outputs and have no capability of rejection for classification. To overcome the non-competence problem and better model the multi-class problem for reducing the classification cost, we embed reject option to ECOC and present a new variant of ECOC algorithm called as Reject-Option-based Re-encoding ECOC (ROECOC). The cost-sensitive classification model and cost-loss function based on Receiver Operating Characteristic (ROC) curve are built respectively. The optimal reject threshold values are obtained by combing the condition to be met for minimizing the loss function and the ROC convex hull. In so doing, reject option ( t 1 , t 2 ) provides a three-symbol output to make dichotomizers more competent and ROECOC more universal and practical for cost-sensitive classification issue. Experimental results on two kinds of datasets show that our scheme with low-degree freedom of initialized ECOC can effectively enhance accuracy and reduce cost.
ℤ<sub>4</sub>-Double Cyclic Codes Are Asymptotically Good

We construct a class of Z4-double cyclic codes generated by pairs of polynomials. Based on the probabilistic method, we prove the asymptotic properties of this class of codes: for any positive real number 0 <; δ <; 1 such that the 4-ary entropy at k+t/2 δ is less than 14, the rate of the random code is convergent to 1/k+t and the relative distance of the code is convergent to δ, where k and l are pairwise coprime positive odd integers. As a result, the Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> -double cyclic codes are asymptotically good.
Mean oriented Riesz features for micro expression classification

Micro-expressions are brief and subtle facial expressions that go on and off the face in a fraction of a second. This kind of facial expressions usually occurs in high stake situations and is considered to reflect a human’s real intent. There has been some interest in micro-expression analysis, however, a great majority of the methods are based on classically established computer vision methods such as local binary patterns, histogram of gradients and optical flow. A novel methodology for micro-expression recognition using the Riesz pyramid, a multi-scale steerable Hilbert transform is presented. In fact, an image sequence is transformed with this tool, then the image phase variations are extracted and filtered as proxies for motion. Furthermore, the dominant orientation constancy from the Riesz transform is exploited to average the micro-expression sequence into an image pair. Based on that, the Mean Oriented Riesz Feature description is introduced. Finally the performance of our methods are tested in two spontaneous micro-expressions databases and compared to state-of-the-art methods.
Efficient Multi-Swarm Binary Harris Hawks Optimization as a Feature Selection Approach for Software Fault Prediction

Designing a solution for an optimization problem requires two main aspects; the optimization technique (e.g., search strategy) and the evaluation criteria (i.e., objective function). In this paper, an enhanced binary version of a recent metaheuristic algorithm, the Harris Hawk Optimization algorithm (EBHHO), is presented to find a (near) optimal solution for the Feature Selection (FS) problem. Moreover, three different classifiers called K-nearest neighbors (kNN), Decision Trees (DT), and Linear Discriminant Analysis (LDA) were used as evaluation criteria to formulate the objective function. In addition to reducing the dimensionality of the dataset using the FS technique, the Adaptive Synthetic (ADASYN) oversampling technique was used to enhance the quality of the learning algorithm by re-balancing the dataset. A set of well-known datasets in the field of Software Fault Prediction (SFP) were used to validate the efficiency of the proposed approach. The obtained results showed that EBHHO is superior over the basic HHO as well as proved the ability of the EBHHO algorithm to produce the best result among a set of well-known optimization methods.
Automatic summarization of scientific articles: A survey

The scientific research process generally starts with the examination of the state of the art, which may involve a vast number of publications. Automatically summarizing scientific articles would help researchers in their investigation by speeding up the research process. The automatic summarization of scientific articles differs from the summarization of generic texts due to their specific structure and inclusion of citation sentences. Most of the valuable information in scientific articles is presented in tables, figures, and algorithm pseudocode. These elements, however, do not usually appear in a generic text. Therefore, several approaches that consider the particularity of a scientific article structure were proposed to enhance the quality of the generated summary, resulting in ad hoc automatic summarizers. This paper provides a comprehensive study of the state of the art in this field and discusses some future research directions. It particularly presents a review of approaches developed during the last decade, the corpora used, and their evaluation methods. It also discusses their limitations and points out some open problems. The conclusions of this study highlight the prevalence of extractive techniques for the automatic summarization of single monolingual articles using a combination of statistical, natural language processing, and machine learning techniques. The absence of benchmark corpora and gold standard summaries for scientific articles remains the main issue for this task.
Efficient Relative Pose Estimation for Cameras and Generalized Cameras in Case of Known Relative Rotation Angle

We propose two minimal solvers to the problem of relative pose estimation for a camera with known relative rotation angle. In practice, such angle can be derived from the readings of a 3D gyroscope. Different from other relative pose formulations fusing a camera and a gyroscope, the use of relative rotation angle does not require extrinsic calibration between the two sensors. The first proposed solver is formulated for a calibrated regular camera and requires four-point correspondences from two views. The second solver extends the problem to a generalized camera and requires five-point correspondences. We represent the rotation part of the motion in terms of unit quaternions in order to construct polynomial equations encoding the epipolar constraints. The Gröbner basis technique is then used to efficiently derive the polynomial solutions. Our first solver for regular cameras significantly improves the existing state-of-the-art solution. The second solver for generalized cameras is novel. The presented minimal solvers can be used in a hypothesize-and-test architecture such as RANSAC for reliable pose estimation. Experiments on synthetic and real datasets confirm that our algorithms are numerically stable, fast, and robust.
Prior Knowledge-Based Optimization Method for the Reconstruction Model of Multicamera Optical Tracking System

The optical tracking system (OTS) plays a vital role in the computer-assisted surgical navigation process, whereas the performance of the commonly used binocular stereo vision is affected by the line-of-sight problem and limited workspace. Thus, this article proposed a prior knowledge-based multicamera reconstruction model (PKRM) to both expand the tracking workspace and improve the tracking robust and computational efficiency of OTS when working in unstructured clinical conditions. This reconstruction model inherits the advantages of the geometrical method, data-driven method, and gating technique (GT). First, we added the geometric principle as the prior knowledge to optimize the training of the multicamera OTS reconstruction model through the Lagrange multiplier method; hence, the prior knowledge feedforward NN (PKFNN) was built. Second, besides the training features, the state of camera (SOC) was extracted in advance to determine the NN structure using GT. According to the SOC feature, the OTS can be self-adaptive to the changing field of view (FOV) caused by optical occlusion, which is frequently occurred in surgery. Furthermore, experiments were carried out to verify the performance of the proposed model, whose accuracy and runtime performed 0.4627 mm and 0.0016 ms, respectively. Results demonstrate that the proposed reconstruction model can achieve higher accuracy and computational efficiency than both the geometrical model and the data-driven model. Especially, by considering SOC as the state prior knowledge, the tracking robustness is enhanced when one or two of the four cameras are not working properly. Note to Practitioners-The original motivation for this article derives from both the line-of-sight limitation and robust demand for optical tracking of surgical instruments. The performance of the multicamera optical tracking system (OTS) depends on its reconstruction model. However, the geometric reconstruction model requires more calculation to obtain high accuracy, which will enlarge the latency and reduce the update rate. In our previous work, the reconstruction model based on the neural network (NN) has achieved accurate tracking in real-time, while the training of the model tends into local optimal values. Hence, we proposed the prior knowledge feedforward NN model to improve the accuracy and computational efficiency. Moreover, to guarantee the line-of-sight in the optical occlusion, the state of camera combining with the gating technique enables the OTS to be self-adaptive for changing the field of view, which greatly ensures the robust tracking process with larger workspace in case of line-of-sight obstructions.
PKE-MET: Public-Key Encryption With Multi-Ciphertext Equality Test in Cloud Computing

Cloud computing enables users to remove the necessity of the need of local hardware architecture, which removes the burden of the users from high computation costs. Therefore, it has attracted much attention and research has been conducted heavily on it. To protect users’ privacy, data is usually encrypted prior to being sent to the cloud server. As the resulting system is unusable, since the cloud can no longer search throughout the data, new cryptographic primitive such as public-key encryption with equality test (PKEET) has been introduced. In PKEET, users can test whether the underlying messages of two ciphertexts encrypted under different public keys are equal or not without the need to decrypt those ciphertexts. This is a very useful tool, especially for the cloud database, since PKEET mainly focuses on the equality test between two ciphertexts. However, in practice, the cloud server may need to verify the equivalence among more than two ciphertexts. This leads to disclosing unnecessary information of users and redundant computation cost will also occur when using traditional PKEET schemes. How to make this more efficient and practical remains an interesting research problem. In this article, to solve the aforementioned problems by providing a novel concept of public-key encryption with multi-ciphertext equality test (PKE-MET). In PKE-MET, each ciphertext can designate a number <inline-formula><tex-math notation="LaTeX">$s$</tex-math></inline-formula> such that the cloud server can only perform equality test on this ciphertext with other <inline-formula><tex-math notation="LaTeX">$s-1$</tex-math></inline-formula> ciphertexts, where all their designated numbers are <inline-formula><tex-math notation="LaTeX">$s$</tex-math></inline-formula> . For PKE-MET, besides traditional OW-CPA and IND-CPA security, we specially define Number security. We instantiate PKE-MET to a concrete scheme and give its security proof. Furthermore, to enable the primitive to be more practical in applications, we extend it to the concept of PKE with flexible MET (PKE-FMET). In PKE-FMET, the cloud server can perform equality test on any number of ciphertexts as long as the maximum number of their designated numbers is less than or equal to the number of ciphertexts. We construct a PKE-FMET scheme based on our PKE-MET construction and prove its security under the defined security models. Besides, the performance analysis mainly of efficiency and security between our constructions and existing equality test schemes in cloud computing show that our proposed schemes are more efficient and secure in the multi-ciphertext scenario.
Faster R-CNN Model With Momentum Optimizer for RBC and WBC Variants Classification

Since many diseases and infections are dependent on the count and type of Red Blood Cells (RBCs) and White Blood Cells (WBCs) present in the blood stream, detection and classification pertaining to them is necessary and relevant. Based from existing related literature, ordinary Neural Networks are usually employed. Also, in existing researches, RBC types are the main focus. Hence, after observing research gaps, a Faster Region-based Convolutional Neural Network (Faster R-CNN) was utilized for this study, focusing not only on RBCs but also on the variants of WBCs. The aim is to have a fast and reliable system in order to achieve the goal of aiding the medical field in the classification of RBCs and WBCs.
Implementation of Residual Network (ResNet) for Devanagari Handwritten Character Recognition

Deeper convolutional neural network architecture has successfully given good results in many applications, but the problem with them is sometimes increasing the number of layers that result in saturation or degradation of the accuracy. This is because of the problem called vanishing gradient. Residual network which was a major breakthrough in the field of neural network helped to overcome this problem and allow to have a deeper network. The current state-of-the-art architecture for handwritten Devanagari character recognition is the convolutional neural network, and attempts were made to make the network deeper; but it also resulted in lower accuracy. Here, we have made an another attempt to use deeper network for this problem but this time using residual network. We have performed the experiments using ResNet 34 and ResNet 50 architecture and compared their result with the current state-of-the-art architecture of convolutional neural networks of two different depths: four layers and eight layers. ‘Devanagari handwritten character dataset’ (DHCD) has been used for obtaining and evaluating the results of the above-mentioned architectures. The experiment proves that the proposed ResNet implementation provides significantly higher accuracy than the current state-of-the-art architecture for the Devanagari handwritten character recognition problem.