Reviewerly/exHarmony huggingface dataset:
Investigation of Strength and Security of Pseudo Random Number Generators

Abstract Security is a key factor in today’s fast communicating world. Many cryptographic algorithms are tested and put into use efficiently. Random numbers are used in diverse forms like nonces, secret key, initialization vector, etc. They find place in encryption, digital signature, hashing algorithms. A deterministic algorithms takes an intial seed value as input and produces pseudo random numbers with falsely induced randomness. This research work extensively surveys large set of state-of-the-art PRNGs and categorizes them based on methodology used to produce them. We compared their statistical results obtained from various statistical test tools like NIST SP 800-22, TestU01. Finally, security analyses of various PRNGs were done quantitatively for their key space, key sensitivity, entropy, speed of bit generation, linear complexity. At last, we concluded the results with some future directions for researchers to carry out their research in improving the PRNGs.
Speeding up the AIFV-2 dynamic programs by two orders of magnitude using Range Minimum Queries

AIFV-2 codes are a new method for constructing lossless codes for memoryless sources that provide better worst-case redundancy than Huffman codes. They do this by using two code trees instead of one and also allowing some bounded delay in the decoding process. Known algorithms for constructing AIFV-codes are iterative; at each step they replace the current code tree pair with a "better" one. The current state of the art for performing this replacement is a pair of Dynamic Programming (DP) algorithms that use O(n5) time to fill in two tables, each of size O(n3) (where n is the number of different characters in the source). This paper describes how to reduce the time for filling in the DP tables by two orders of magnitude, down to O(n3). It does this by introducing a grouping technique that permits separating the Θ(n3)-space tables into Θ(n) groups, each of size O(n2), and then using Two-Dimensional Range-Minimum Queries (RMQs) to fill in that group's table entries in O(n2) time.
Leaking Information Through Cache LRU States in Commercial Processors and Secure Caches

The Least-Recently Used (LRU) cache replacement policy and its variants are widely deployed in modern processors. This article shows in detail that the LRU states of caches can be used to leak information: any access to a cache by a sender will modify the LRU state, and the receiver is able to observe this through a timing measurement. This article presents LRU timing-based channels both when the sender and the receiver have access to shared memory, e.g., shared library, and when they are separate processes without shared memory. In addition, the new LRU timing-based channels are demonstrated on both Intel and AMD processors in scenarios where the sender and the receiver are sharing the cache in both hyper-threaded setting and time-sliced setting. The transmission rates of the LRU channels can be up to 600 Kbps per cache set in the hyper-threaded setting. Different from the majority of existing cache channels which require the sender to trigger cache misses, the new LRU channels work with the sender only having cache hits, making the channel faster and stealthier. This article further discusses the effectiveness of the new LRU channels against a number of secure cache designs. Especially, the LRU channels are demonstrated to work against two representative secure caches, Partition-Locked (PL) cache and Random Fill (RF) cache, in the gem5 simulator, showing possible vulnerabilities in the secure cache designs in which the security of the replacement state is not protected properly.
Single Maritime Image Defogging Based on Illumination Decomposition Using Texture and Structure Priors

Removing fog components from maritime images is more challenging than removing them from land images because of the complicated scattering effects. In this paper, we propose a single maritime image defogging algorithm based on illumination decomposition. First, we decompose a foggy input image into glow and glow-free layers by formulating an optimization problem and then solving it effectively, where the optimization variables are updated by closed-form solutions. Then, the fog components in the glow-free layer are removed using a defogging algorithm. Finally, the natural illumination in the glow layer is recovered using a compensation scheme to obtain the final fog-free image. Experimental results on real test images demonstrate that the proposed algorithm outperforms state-of-the-art algorithms in terms of both subjective and objective evaluation criteria.
Medical Image Compression Based on Wavelets with Particle Swarm Optimization

This paper presents a novel method utilizing wavelets with particle swarm optimization (PSO) for medical image compression. Our method utilizes PSO to overcome the wavelets discontinuity which occurs when compressing images using thresholding. It transfers images into subband details and approximations using a modified Haar wavelet (MHW), and then applies a threshold. PSO is applied for selecting a particle assigned to the threshold values for the subbands. Nine positions assigned to particles values are used to represent population. Every particle updates its position depending on the global best position (gbest) (for all details subband) and local best position (pbest) (for a subband). The fitness value is developed to terminate PSO when the difference between two local best (pbest) successors is smaller than a prescribe value. The experiments are applied on five different medical image types, i.e., MRI, CT, and X-ray. Results show that the proposed algorithm can be more preferably to compress medical images than other existing wavelets techniques from peak signal to noise ratio (PSNR) and compression ratio (CR) points of views.
Multi-Metric Re-Identification for Online Multi-Person Tracking

Multi-person tracking plays a vital role in intelligent video surveillance systems and has attracted researchers’ growing attention in recent years. This paper proposes a tracking-by-detection method to detect and track all existing persons in video sequences. The proposed method re-identifies detected persons in the latest video frame as observed persons in previous frames and thus generates their trajectories. Re-identification of the proposed approach uses a fusion of six distance metrics. Four metrics, i.e., position, scale, distance to estimated position, and tracklet continuity, are derived from two motion-based features, and two metrics, i.e., dominant colors and histogram of oriented gradients, are derived from corresponding appearance-based features. The proposed method performs tracking in two general steps per each frame. In the first step, all persons in the video frame are detected using the state-of-the-art YOLOv3 object detector. In the second step, the re-identification algorithm generates correspondences between detected persons in the latest video frame and observed persons in previous frames, using the distance matrix built up from compound distances between all <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">detected</i> – <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">observed person</i> pairs. Experimental results show that our simple yet effective approach achieves significant performance in multi-person tracking compared to existing state-of-the-art methods.
Preventing Identity Theft: Perspectives on Technological Solutions from Industry Insiders

An estimated 26 million American citizens per year have been victims of an identity-based crime.This study contributes to the scholarship on financial crimes facilitated through identity-based criminal activity by examining the views on technological approaches to the prevention of identity theft among 50 professionals working in the identitybased crime victim services, including those from the public sector and private industry.The professionally diverse sample included private investigators, fraud examiners, victim service providers, and executives of firms offering victim services and protection services.Data were collected during a series of focus groups held at professional conferences.The paper reports on the views held by focus group participants on specific technological solutions to identity theft victimization, including biometric scanning, dark web scanning, subscriptionbased monitoring programs, and broader thematic observations about the current environment for protecting information, emerging sources of risk, and policy recommendations identified by focus group participants.Perspectives from insiders about the pros and cons of each approach can provide researchers and enforcement agencies with enhanced capacity to avoid empty technological promises that fail to protect victims from subsequent victimizations.
Deep Convolutional Neural Network for Computer-Aided Detection of Breast Cancer Using Histopathology Images

Abstract The innovation in medical imaging technologies leads to a frenetic pace of change in health care. In recent years various deep learning algorithms play a significant role in medical image classification and diagnosis. The deep convolutional neural network (DCNN) has obtained impressive results in many health-related applications. The fine-tuning parameters and weight initialization is the major task to adapt pre-trained convolution models. We explored transfer learning approaches using Alexnet, and VGG-16 analyzed with their behavior. Also, the DCNN framework had developed and compared with Alex net and VGG-16 transfer learning models. The DCNN attained more significant results compare to transfer learning models. The DCNN procures outstanding performance for binary (93.38%) and multi-class (average 89.29%), which exceeds the previous state of the art techniques in the literature.
BFSN: A Novel Method of Encrypted Traffic Classification Based on Bidirectional Flow Sequence Network

With the rapid development of network technology and encryption technology, network security issues have received more and more attention, and network encryption traffic is increasing, which results in a huge challenge for network traffic classification. Combining machine learning algorithms with manual design has become a mainstream approach to solve this problem, However, it requires a large amount of human effort to extract and process features, which depend on professional experience heavily. In this paper, We discuss the essential reason why convolutional neural network(CNN) can deal with the problem of encrypted traffic classification and propose a novel classification framework the Bidirectional Flow Sequence Network(BFSN) based on long short-term memory (LSTM). Compared with the traditional traffic classification scheme, the BFSN is an end-to-end classification model that learns representative features from the raw traffic and classifies them. Moreover, We apply the length and direction information of the encrypted traffic to construct the bidirectional traffic sequence and then process it based on LSTM. Our Experiments gains the excellent accuracy about 91% based the ISCX VPN-NonVPN dataset.
Effectiveness Analysis of Distributed Scrum Model Compared to Waterfall approach in Third-Party Application Development

Scrum is widely used to manage application development, including third-party vendors. There are some problems when third-party vendor using scrum such as communication challenges, lack of control, and lack of trust. Therefore, we proposed new model of distributed scrum using Scrum of Scrum and Holonic view to manage an application development project in third party vendors. In this study we created 3 model, Scrum Team Model, Meeting Coordination Model, and Product Backlog Model. In this study it has been shown that the Distributed scrum method is better than waterfall method as a result of the project’s success based on scope, time, and cost aspects. Based on the comparison result, it is found that the Cost Performance Index (CPI) and the Schedule Performance Index (SPI) value of the waterfall approach project is smaller than the Distributed scrum approach project as well as the change request issued by the Distributed scrum project resulting is less than the waterfall project.
Understanding the Potential Risks of Sharing Elevation Information on Fitness Applications

The extensive use of smartphones and wearable devices has facilitated many useful applications. For example, with Global Positioning System (GPS)-equipped smart and wearable devices, many applications can gather, process, and share rich metadata, such as geolocation, trajectories, elevation, and time. For example, fitness applications, such as Runkeeper and Strava, utilize information for activity tracking, and have recently witnessed a boom in popularity. Those fitness tracker applications have their own web platforms, and allow users to share activities on such platforms, or even with other social network platforms. To preserve privacy of users while allowing sharing, several of those platforms may allow users to disclose partial information, such as the elevation profile for an activity, which supposedly would not leak the location of the users. In this work, and as a cautionary tale, we create a proof of concept where we examine the extent to which elevation profiles can be used to predict the location of users. To tackle this problem, we devise three plausible threat settings under which the city or borough of the targets can be predicted. Those threat settings define the amount of information available to the adversary to launch the prediction attacks. Establishing that simple features of elevation profiles, e.g., spectral features, are insufficient, we devise both natural language processing (NLP)-inspired text-like representation and computer vision-inspired image-like representation of elevation profiles, and we convert the problem at hand into text and image classification problem. We use both traditional machine learning-and deep learning-based techniques, and achieve a prediction success rate ranging from 59.59% to 95.83%. The findings are alarming, and highlight that sharing elevation information may have significant location privacy risks.
ACDnet: An action detection network for real-time edge computing based on flow-guided feature approximation and memory aggregation

Interpreting human actions requires understanding the spatial and temporal context of the scenes. State-of-the-art action detectors based on Convolutional Neural Network (CNN) have demonstrated remarkable results by adopting two-stream or 3D CNN architectures. However, these methods typically operate in a non-real-time, ofline fashion due to system complexity to reason spatio-temporal information. Consequently, their high computational cost is not compliant with emerging real-world scenarios such as service robots or public surveillance where detection needs to take place at resource-limited edge devices. In this paper, we propose ACDnet, a compact action detection network targeting real-time edge computing which addresses both efficiency and accuracy. It intelligently exploits the temporal coherence between successive video frames to approximate their CNN features rather than naively extracting them. It also integrates memory feature aggregation from past video frames to enhance current detection stability, implicitly modeling long temporal cues over time. Experiments conducted on the public benchmark datasets UCF-24 and JHMDB-21 demonstrate that ACDnet, when integrated with the SSD detector, can robustly achieve detection well above real-time (75 FPS). At the same time, it retains reasonable accuracy (70.92 and 49.53 frame mAP) compared to other top-performing methods using far heavier configurations. Codes will be available at https://github.com/dginhac/ACDnet.
The rising trend of fraud and forgery in Pakistan’s banking industry and precautions taken against

Purpose This study aims to investigate the involvement of employees in frauds and forgeries in banking industry of Pakistan and precautions taken against it. This research explored the types of frauds prevailing in Pakistan’s banking industry, and the causes of employee involvement in frauds. Design/methodology/approach In-depth interviews with the officers working in fraud/compliance/risk department at commercial banks as well as the officials working in the inspection and policymaking departments at the State Bank of Pakistan (SBP) were conducted. Research questions were developed under the guidance of experts working in the banking industry, so the research possesses internal validity. Data was analyzed using thematic analysis. Findings This study revealed that the SBP has devised many policies and guidelines for commercial banks against fraud management, but these are not properly implemented. These policies also include precautionary measures, which are recommended by the SBP to lessen fraud. Besides this, banks are also taking initiatives of their own to control the rising trend of frauds and forgeries. At the end, brief conclusion and effective recommendations are given to the practitioners, policymakers and management. Originality/value To the best of the author’s knowledge, this area of management has not been explored by researchers in Pakistan; hence, this research provides valuable information to bank managers, risk management departments, risk avoidance policymakers, bank shareholders, depositors, borrowers and government agencies. This study provides deep insights into the prevalence of frauds in the banking industry of Pakistan.
Algebraic Cryptanalysis of O'zDSt 1105:2009 Encryption Algorithm

In this paper, we examine algebraic attacks on the O'zDSt 1105:2009. We begin with a brief review of the meaning of algebraic cryptanalysis, followed by an algebraic cryptanalysis of O'zDSt 1105:2009. Primarily O'zDSt 1105:2009 encryption algorithm is decomposed and each transformation in it is algebraic described separately. Then input and output of each transformation are expressed with other transformation, encryption key, plaintext and cipher text. Created equations, unknowns on it and degree of unknowns are analyzed, and then overall result is given. Based on experimental results, it is impossible to save all system of equations that describes all transformations in O'zDSt 1105:2009 standard. Because, this task requires 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">73</sup> bytes for the second round. For this reason, it is advisable to evaluate the parameters of the system of algebraic equations, representing the O'zDSt 1105:2009 standard, theoretically.
Dynamic Precision Analog Computing for Neural Networks

Analog electronic and optical computing exhibit tremendous advantages over digital computing for accelerating deep learning when operations are executed at low precision. Although digital architectures support programmable precision to increase efficiency, analog computing architectures today only support a single, static precision. In this work, we characterize the relationship between the effective number of bits (ENOB) of precision of analog processors, which is limited by noise, and digital bit precision for quantized neural networks. We propose extending analog computing architectures to support dynamic levels of precision by repeating operations and averaging the result, decreasing the impact of noise. To utilize dynamic precision, we propose a method for learning the precision of each layer of a pre-trained model without retraining network weights. We evaluate this method on analog architectures subject to shot noise, thermal noise, and weight noise and find that employing dynamic precision reduces energy consumption by up to 89% for computer vision models such as Resnet50 and by 24% for natural language processing models such as BERT. In one example, we apply dynamic precision to a shot-noise limited homodyne optical neural network and simulate inference at an optical energy consumption of 2.7 aJ/MAC for Resnet50 and 1.6 aJ/MAC for BERT with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${&lt; }2\%$</tex-math></inline-formula> accuracy degradation, implying that the optical energy consumption is unlikely to be the dominant cost.
An Integrated Method of Ship Detection and Recognition in Sar Images based on Deep Learning

Ship target interpretation in SAR images has become an important topic in research in recent years. With the improvement of SAR image resolution, the performance of traditional automatic target recognition (ATR) method decreases gradually. The emergence of deep network provides a new solution for SAR image ship interpretation. Ship target interpretation in SAR images based on deep learning is divided into detection and classification, but they haven't been integrated yet. Based on the process flow of traditional ATR system, an integration method of ship target detection and recognition based on deep network is proposed in this paper. And at the end of the network, the squeeze-and-excitation (SE) module is added to the classification subnetwork. The effectiveness of the proposed integration method is verified by experiments, and the classification accuracy of the ship increased by 3.7% after adding SE module.
Design of an expert system architecture: An overview

Abstract An area of Artificial intelligence (AI) that can claim a large measure of responsibility for the current heightened AI awareness is Knowledge Based Expert System (KBES), computer programs that embody human expertise. KBES specifically enable the computer to assist in solving ill-structured and non-deterministic problems such as design activities including synthesis, evaluation, modeling and decision-making process. The paper explains the role of Backward Chaining method, Forward Chaining method, Rule-Value method as three major methods involved in solving these problems to differentiate the Knowledge Based Expert System programming from normal programming. The development of an expert system requires a tool that will provide for better means of knowledge acquisition, inference mechanism and user interface. This paper brings out an overview of Expert System, a systematic study involving a critical analysis of the Expert System (ES) development, specifying its requirements. Further it highlights the features of Expert System and its implementation.
Distilling Location Proposals of Unknown Objects through Gaze Information for Human-Robot Interaction

Successful and meaningful human-robot interaction requires robots to have knowledge about the interaction context - e.g., which objects should be interacted with. Unfortunately, the corpora of interactive objects is - for all practical purposes - infinite. This fact hinders the deployment of robots with pre-trained object-detection neural networks other than in pre-defined scenarios. A more flexible alternative to pre-training is to let a human teach the robot about new objects after deployment. However, doing so manually presents significant usability issues as the user must manipulate the object and communicate the object's boundaries to the robot. In this work, we propose streamlining this process by using automatic object location proposal methods in combination with human gaze to distill pertinent object location proposals. Experiments show that the proposed method 1) increased the precision by a factor of approximately 21 compared to location proposal alone, 2) is able to locate objects sufficiently similar to a state-of-the-art pre-trained deep-learning method (FCOS) without any training, and 3) detected objects that were completely missed by FCOS. Furthermore, the method is able to locate objects for which FCOS was not trained on, which are undetectable for FCOS by definition.