Illegal benefit in anti-corruption legislation: problems of legal regulation

The types and meaning of the main methods of preventing corruption are examined in the article. Administrative methods of combating corruption are identified among them. They are divided into two groups: rule-making (adoption of relevant law); law enforcement (application of law). The importance of proper legislative regulation in liquidating corruption is outlined. The formation of anti-corruption legislation, which has been going on for a long time, is revealed and it is noted that to date anti-corruption legislation has undergone a significant transformation and changed its purpose that is “prevention” instead of “struggle”. Emphasis is placed on innovations and reforms contained in the updated anti-corruption legislation. The provisions of statutary acts, which enshrine illegal benefit, are given. For carrying out a detailed analysis, the scholars’ positions on the current legislation on the definition of “illegal benefit” were analyzed. It revealed differences in existing approaches to the essential features of this phenomenon. The concept and features of a gift are analyzed in order to distinguish it from the illegal benefit and identify the main reasons for this. The importance of clear regulation of illegal benefit and gifts in order to avoid incorrect classification of violated anti-corruption legislation was emphasized. After analyzing the essence of “illegal benefit” and “gift”, it was found out that common to the subject of gift and illegal benefit was to receive both materially defined and things that do not have a monetary equivalent and material reflection. A sign that allows to distinguish a gift from an illegal benefit is the sign “without any legal grounds” for receiving an illegal benefit and “gratuitousness and receipt/gift at a price below the minimum market”. It is the last part of the component definition of a gift that testifies to the value characteristics of a gift. It is noted that taking into account the fact that since the difference between a gift and an illegal benefit is unclear, it may lead to incorrect classification of violated anti-corruption legislation. Key words: illegal benefit, gift, anti-corruption legislation, corruption offense, corruption-related offenses, criminal liability, administrative liability.

Using Progressive Context Encoders for Anomaly Detection in Digital Pathology Images

Whole slide imaging (WSI) is transforming the practice of pathology, converting a qualitative discipline into a quantitative one. However, one must exercise caution in interpreting algorithm assertions, particularly in pathology where an incorrect classification could have profound impacts on a patient, and rare classes exist that may not have been seen by the algorithm during training. A more robust approach would be to identify areas of an image for which the pathologist should concentrate their effort to make a final diagnosis. This anomaly detection strategy would be ideal for WSI, but given the extremely high resolution and large file sizes, such an approach is difficult. Here, we combine progressive generative adversarial networks with a flexible adversarial autoencoder architecture capable of learning the normal distribution of WSIs of normal skin tissue at extremely high resolution and demonstrate its anomaly detection performance. Our approach yielded pixel-level accuracy of 89% for identifying melanoma, suggesting that our label-free anomaly detection pipeline is a viable strategy for generating high quality annotations - without tedious manual segmentation by pathologists. The code is publicly available at https://github.com/Steven-N-Hart/P-CEAD.

Handwritten Character Recognition on Android for Basic Education Using Convolutional Neural Network

An international initiative called Education for All (EFA) aims to create an environment in which everyone in the world can get an education. Especially in developing countries, many children lack access to a quality education. Therefore, we propose an offline self-learning application to learn written English and basic calculation for primary level students. It can also be used as a supplement for teachers to make the learning environment more interactive and interesting. In our proposed system, handwritten characters or words written on tablets were saved as input images. Then, we performed character segmentation by using our proposed character segmentation methods. For the character recognition, the Convolutional Neural Network (CNN) was used for recognizing segmented characters. For building our own dataset, handwritten data were collected from primary level students in developing countries. The network model was trained on a high-end machine to reduce the workload on the Android tablet. Various types of classifiers (digit and special characters, uppercase letters, lowercase letters, etc.) were created in order to reduce the incorrect classification. According to our experimental results, the proposed system achieved 95.6% on the 1000 randomly selected words and 98.7% for each character.

Should We Just Let the Machines Do It? The Benefit and Cost of Action Recommendation and Action Implementation Automation

Objective To examine the effects of action recommendation and action implementation automation on performance, workload, situation awareness (SA), detection of automation failure, and return-to-manual performance in a submarine track management task. Background Theory and meta-analytic evidence suggest that with increasing degrees of automation (DOA), operator performance improves and workload decreases, but SA and return-to-manual performance declines. Method Participants monitored the location and heading of contacts in order to classify them, mark their closest point of approach (CPA), and dive when necessary. Participants were assigned either no automation, action recommendation automation, or action implementation automation. An automation failure occurred late in the task, whereby the automation provided incorrect classification advice or implemented incorrect classification actions. Results Compared to no automation, action recommendation automation benefited automated task performance and lowered workload, but cost nonautomated task performance. Action implementation automation resulted in perfect automated task performance (by default) and lowered workload, with no costs to nonautomated task performance, SA, or return-to-manual performance compared to no automation. However, participants provided action implementation automation were less likely to detect the automation failure compared to those provided action recommendations, and made less accurate classifications immediately after the automation failure, compared to those provided no automation. Conclusion Action implementation automation produced the anticipated benefits but also caused poorer automation failure detection. Application While action implementation automation may be effective for some task contexts, system designers should be aware that operators may be less likely to detect automation failures and that performance may suffer until such failures are detected.

DETERMINATION OF PREMEDITATED MURDER FROM SUICIDE AND ACCIDENT: THEORETICAL AND METHODOLOGICAL APPROACHES

The article considers topical issues of definition and qualification of premeditated murder. Intentional homicide has been shown to be distinguished from related and externally similar events, such as suicide and accident. This issue is especially relevant given the fact that a person who committed premeditated murder may knowingly seek to create an environment for incorrect classification of the act. Therefore, it is not possible and not necessary to draw unambiguous conclusions about the legal qualification of such an event solely due to objective circumstances, despite the sometimes obvious nature of the event. The author identified a number of common and distinctive features that should be taken into account by law enforcement agencies during the qualification of an event involving a violent death. An example is the presence and clarity of the reasons for which a person may have committed a probable suicide. However, it is stated that a person's subjective attitude to the act and external signs should be considered only in conjunction with other circumstances. The author also considers certain features of the object, subject, objective and subjective side of premeditated murder, which will be key in resolving the issue of qualification. It was found that the essential features of premeditated murder are its illegality and the intention to take the life of another person, is only in the presence of both of these factors can a legal fact be interpreted as premeditated murder. There are certain signs of an accident that distinguish such an event from premeditated murder. The key is the absence of any form of guilt on the part of the perpetrator. The author also identified a number of features in the presence of which the qualification of the act as premeditated murder should be excluded, for example, the case when the subject of the act and the victim are one and the same person.

Does the incorrect classification of misconduct charges constitute substantive unfairness? EOH Abantu v CCMA (2019) 40 ILJ 2477 (LAC)

Dismissals are commonplace in employment and arise for various reasons. One such reason is the unacceptable or undesirable conduct of an employee, which is recognised as a dismissal for misconduct. Notwithstanding the employers’ right to effect dismissals, employees are considerably protected by the law (s 185 of the Labour Relations Act (LRA)). An employee has the right to challenge his/her dismissal by referring an unfair dismissal dispute to the CCMA (s 191 of the LRA). This is not surprising considering the fact that fairness is the cornerstone of the employment relationship (as evident from s 23(1) of the Constitution, which states that “everyone has the right to fair labour practices”; see also Blanpain and Weiss Changing Industrial Relations and Modernisation of Labour Law (2003) 182). While it is indisputable that employers should act fairly towards its employees, a significant principle that has been highlighted in the determination of fairness is that it must accommodate and balance the conflicting interests and rights of both employers and employees (National Education Health & Allied Workers Union v University of Cape Town (2003) 24 ILJ 95 (CC) par 38 and 40).

Clonal Hematopoiesis in Liquid Biopsy: From Biological Noise to Valuable Clinical Implications

The use of blood liquid biopsy is being gradually incorporated into the clinical setting of cancer management. The minimally invasive nature of the usage of cell-free DNA (cfDNA) and its ability to capture the molecular alterations of tumors are great advantages for their clinical applications. However, somatic mosaicism in plasma remains an immense challenge for accurate interpretation of liquid biopsy results. Clonal hematopoiesis (CH) is part of the normal process of aging with the accumulation of somatic mutations and clonal expansion of hematopoietic stem cells. The detection of these non-tumor derived CH-mutations has been repeatedly reported as a source of biological background noise of blood liquid biopsy. Incorrect classification of CH mutations as tumor-derived mutations could lead to inappropriate therapeutic management. CH has also been associated with an increased risk of developing cardiovascular disease and hematological malignancies. Cancer patients, who are CH carriers, are more prone to develop therapy-related myeloid neoplasms after chemotherapy than non-carriers. The detection of CH mutations from plasma cfDNA analysis should be cautiously evaluated for their potential pathological relevance. Although CH mutations are currently considered as “false-positives” in cfDNA analysis, future studies should evaluate their clinical significance in healthy individuals and cancer patients.

Modelling militarized interstate disputes using data mining techniques: Prevention and prediction of conflicts

The use of modern data mining techniques on large datasets has become a recent phenomenon across a broad range of applications. One of the most frequent tasks is to build statistical models using historical data and utilize them to predict new, so far unclassified, cases. This article examines the problem of predicting a military interstate dispute between two states (dyad) by employing selected data mining techniques. Suitable methods are identified and applied to the existing dataset of politically relevant dyads. The result is the building of statistical models for the classification of potential dyadic conflicts. The overall performance of these models is verified and cost analysis is done based on the different impacts of incorrect classification. The results are compared with those of other published research studies in the field of conflict prediction; the models created by data mining techniques significantly outperform all rival algorithms and approaches. Finally, the last part of the article presents the results of applying data mining techniques to association, i.e. to discovering relationships and dependencies in the data.